< Dongguk Open Databases & CNN Model >

 

---------------------------------------------

46. Dongguk Modified CycleGAN for Age Estimation (DMC4AE) and Generated Images

45. Dongguk Vess-Net Models with Algorithm

44. Dongguk CNN for Detecting Road Markings Based on Adaptive ROI with Algorithms

43. Dongguk CNN stacked LSTM and CycleGAN for Action Recognition, Generated Data, and Dongguk Activities & Actions Database (DA&A-DB2)

42. Dongguk generation model of presentation attack face images (DG_FACE_PAD_GEN)

41. Label Information of Sun Yat-sen University Multiple Modality Re-ID (SYSU-MM01) Database and Dongguk Gender Recognition CNN Models (DGR-CNN).

40. Dongguk cGAN-based Iris Image Generation Model and Generated Images (DGIM&GI)

39. Dongguk CNN and LSTM models for the classification of multiple gastrointestinal (GI) diseases, and video indices of experimental endoscopic videos

38. Dongguk Dual-Camera-based Gaze Database (DDCG-DB1) and CNN models with Algorithms

37. Dongguk Mobile Finger-Wrinkle Database (DMFW-DB1) and CNN models with Algorithms

36. Dongguk low-resolution drone camera dataset & CNN models

35. Dongguk CNN Model for CBMIR

34. Dongguk Person ReID CNN Models (DPRID-CNN)

33. Dongguk DenseNet-based Finger-vein Recognition Model (DDFRM) with algorithms

32. Dongguk OR-Skip-Net Model for Image Segmentation with Algorithm and Black Skin People (BSP) Label Information

31. Dongguk Banknote Type and Fitness Database (DF-DB3) & CNN Model with algorithms

30. Dongguk RetinaNet for Detecting Road Marking Objects with Algorithms and Annotated Files for Open Databases

29. Dongguk CNN Model for NIR Ocular Recognition (DC4NO) with algorithm

28. Dongguk Face Presentation Attack Detection Algorithms by Spatial and Temporal Information (DFPAD-STI)

27. Dongguk Dual Camera-based Driver Database (DDCD-DB1) and Trained Faster R-CNN Model with Algorithm

26. Dongguk FRED-Net with Algorithm

25. Dongguk Face and Body Database (DFB-DB1) with CNN models and algorithms

24. Dongguk Night-Time Face Detection database (DNFD-DB1) and algorithm including CNN model

23. Dongguk Iris Spoof Detection CNN Model version 2 (DFSD-CNN-2) with Algorithm

22. Dongguk Fitness Database (DF-DB2) & CNN Model

21. Dongguk-body-movement-based human identification

database version 2 (DBMHI-DB2) & CNN Model

20. Dongguk Multimodal Recognition CNN of Finger-vein and Finger shape (DMR-CNN) with Algorithm

19. Dongguk Drone Camera Database (DDroneC-DB2) with CNN models

18. Dongguk Periocular Database (DP-DB1) with CNN models and algorithms

17. Dongguk IrisDenseNet CNN Model (DI-CNN) with Algorithm

16. Dongguk Visible Light Iris Recognition CNN Model (DVLIR-CNN)

15. Dongguk Aggressive and Smooth Driving Database (DASD-DB1)

and CNN Model

14. Dongguk Night-time Pedestrian Detection Faster R-CNN and

Algorithm

13. Dongguk Shadow Detection Database (DSDD-DB1) & CNN

Model

12. Dongguk driver gaze classification database (DDGC-DB1) and

CNN model

11. Dongguk Age Estimation CNN Model (DAE-CNN)

10. Dongguk Single Camera-based Driver Database (DSCD-DB1)

9. ISPR Database (real and presentation attack finger-vein images)

& Algorithm Including CNN Model

8. Dongguk Visible Light & FIR Pedestrian Detection Database

(DVLFPD-DB1) & CNN Model

7. Dongguk Open and Closed Eyes Database (DOCE-DB1) & CNN

Model

6. Dongguk Multi-national Currencies Database (DMC-DB1) & CNN

Model

 

 

46. Dongguk Modified CycleGAN for Age Estimation (DMC4AE) and Generated Images

 

(1) Introduction

We trained our modified CycleGAN models for age estimation with heterogeneous databases of MegaAge and MORPH databases [1,2]. We made our trained models and generated images by modified CycleGAN open to other researchers.

 

1.    Y. Zhang, L. Liu, C. Li, and C.C. Loy, Quantifying facial age by posterior of age comparisons, In Proceedings of British Machine Vision Conference, London, UK, 4-7 September 2017; pp. 1-14.

2.    K. Ricanek and T. Tesafaye, Morph: A longitudinal image database of normal adult age-progression, In Proceedings of 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK, 10-12 April 2006; pp 341345.

 

(2) Request for our models and images

To gain access to our models and images, download the following request form. Please sign and scan the request form and email to Mr. Yu Hwan Kim (taekkuon@dongguk.edu).

 

Any work that uses these models and images must acknowledge the authors by including the following reference.

 

Yu Hwan Kim, Min Beom Lee, Se Hyun Nam, and Kang Ryoung Park, Enhancing the Accuracies of Age Estimation with Heterogeneous Databases Using Modified CycleGAN, IEEE Access, in submission.

 

< Request Form for DMC4AE and Generated Images >

 

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45. Dongguk Vess-Net Models with Algorithm

 

(1) Introduction

We trained our Vess-Net model based on dual stream feature empowerment scheme for retinal vessel segmentation to aid the process of diagnosing diseases like diabetic and hypertensive retinopathy. In our experiments we validated the performance of our method with three different publicly available fundus image databases including DRIVE [1] CHASE-DB1 [2] and STARE [3]. We made our trained models open to other researchers.

 

3.    Gastrolab Staal, J.; Abràmoff, M.D.; Niemeijer, M.; Viergever, M.A.; van Ginneken, B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging, 2004, 23, 501509.

4.    Fraz, M.M.; Remagnino, P.; Hoppe, A.; Uyyanonvara, B.; Rudnicka, A.R.; Owen, C.G.; Barman, S.A. An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 2012, 59, 25382548

5.    Hoover, A.; Kouznetsova, V.; Goldbaum, M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Image, 2000, 19, 203-210.

 

(2) Request for our Vess-Net models

To gain access to the Vess-Net trained models, download the following request form. Please sign and scan the request form and email to Mr. Muhammad Arsalan (arsal@dongguk.edu).

 

Any work that uses these Vess-Net models must acknowledge the authors by including the following reference.

 

Muhammad Arsalan, Muhammad Owais, Tahir Mahmood, Se Woon Cho and Kang Ryoung Park, Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-based Semantic Segmentation, Journal of Clinical Medicine, Vol.  8, Issue 9(1446), pp. 1-27, September 2019.

 

< Request Form for Vess-Net Models >

 

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44. Dongguk CNN for Detecting Road Markings Based on Adaptive ROI with Algorithms

(1) Introduction

We created adaptive ROI images before using them to train our convolutional neural network (CNN). In the first stage, a vanishing point is detected in order to create the ROI image. The ROI image that covers the majority of the road region is then used as the input to train the CNN-based detector and classifier in the second stage. We made the models, generated data, and labeled information of database open to other researchers. Our CNN model was trained with Malaga urban dataset [1], the Daimler dataset [2], and the Cambridge dataset [3].

 

1. The Málaga Stereo and Laser Urban Data Set MRPT. Available online: https://www.mrpt.org/MalagaUrbanDataset (accessed on 1 October 2018).

2. Daimler Urban Segmentation Dataset.

Available online: http://www.6d-vision.com/scene-labeling (accessed on 2 January 2019).

3. Cambridge-driving Labeled Video Database (CamVid). Available online: http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/ (accessed on 1 October 2018).

 

(2) Request for models, generated data, and labeled information

To obtain our pretrained model, generated data, and labeled information, please fill the request form bellow and send an email to Dr. Toan Minh Hoang at hoangminhtoan@dongguk.edu. Any work that uses the provided pretrained network must acknowledge the authors by including the following reference.

 

Toan Minh Hoang, Se Hyun Nam, and Kang Ryoung Park, Enhanced Detection and Recognition of Road Markings Based on Adaptive Region of Interest and Deep Learning,IEEE Access, Vol. 7, pp. 109817- 109832, August 2019.

 

 

 

< Request Form for Models, Generated Data, and Labeled Information >

 

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43. Dongguk CNN stacked LSTM and CycleGAN for Action Recognition, Generated Data, and Dongguk Activities & Actions Database (DA&A-DB2)

 

(1) Introduction

We trained our convolutional neural network (CNN), CNN stacked with long short-term memory (CNN-LSTM), cycle-consistent adversarial network (CycleGAN) models with our action database. We made the models, generated data, and database open to other researchers.

 

(2) Request for Models, Generated Data, and DA&A-DB2

To obtain our pretrained model, generated data, and database, please fill the request form bellow and send an email to Dr. Batchuluun at ganabata87@dongguk.edu. Any work that uses the provided pretrained network must acknowledge the authors by including the following reference.

 

Ganbayar Batchuluun, Dat Tien Nguyen, Tuyen Danh Pham, Chanhum Park, and Kang Ryoung Park, Action Recognition from Thermal Videos, IEEE Access, Vol. 7, pp. 103893- 103917, August 2019.

 

 

 

< Request Form for Models, Generated Data, and DA&A-DB2 >

 

Please complete the following form to request access to our pretrained model, generated data, and database (All contents must be completed). This model, data, and database should not be used for commercial use.

 

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42. Dongguk generation model of presentation attack face images (DG_FACE_PAD_GEN)

 

(1) Introduction

We trained our generative adversarial network (GAN)-based model to artificially generate presentation attack (PA) face images to reduce the efforts of PA image acquisition.

 

(2) Request for obtaining DG_FACE_PAD_GEN

 

To obtain our pretrained model, please fill the request form bellow and send an email to Mr. Nguyen at nguyentiendat@dongguk.edu. Any work that uses the provided pretrained network must acknowledge the authors by including the following reference.

 

Dat Tien Nguyen, Tuyen Danh Pham, Ganbayar Batchuluun, Kyoung Jun Noh, and Kang Ryoung Park, Presentation Attack Face Image Generation Based on Deep Generative Adversarial Network, Sensors, in preparation for submission.

 

 

< Request Form for DG_FACE_PAD_GEN >

 

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41. Label Information of Sun Yat-sen University Multiple Modality Re-ID (SYSU-MM01) Database and Dongguk Gender Recognition CNN Models (DGR-CNN).

 

(1) Introduction

We collected gender information of Sun Yat-sen University Multiple Modality Re-ID (SYSU-MM01) database and trained gender recognition system based on ResNet-101 using two databases including the SYSU-MM01 and the Dongguk Body-based Gender Database (DBGender-DB2). We made label information of SYSU-MM01 database and Dongguk Gender Recognition CNN (DGR-CNN) open to other researchers.

 

(2) Request for Label Information and DGR-CNN

To gain access to the label information and DGR-CNN, download the following request form for label information of SYSU-MM01 and DGR-CNN. Please sign and scan the request form and email to Ms. Na Rae Baek (naris27@dongguk.edu).

 

Any work that uses the label information of SYSU-MM01 database or this CNN model must acknowledge the authors by including the following reference.

Na Rae Baek, Se Woon Cho, Ja Hyung Koo, Noi Quang Truong, and Kang Ryoung Park, Multimodal Camera-based Gender Recognition Using Human-body Image with Two-step Reconstruction Network, IEEE Access, Vol. 7, pp. 104025-104044, August 2019.

 

 

< Request Form for label information of SYSU-MM01 and DGR-CNN >

 

Please complete the following form to request access to the label information of SYSU-MM01 and DGR-CNN. These files should not be used for commercial use.

 

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40. Dongguk cGAN-based Iris Image Generation Model and Generated Images (DGIM&GI)

 

(1) Introduction

We trained generation models based on cGAN (pix2pix model) using NICE.II training dataset (selected from UBIRIS.v2) and MICHE database on visible light environment and CASIA-Iris-Distance database on NIR environment, respectively. Additionally, we generated iris images using trained generation models with each database. We made DGIM (trained generation models) and GI (generated images from trained model) open to other researchers.

 

(2) Request for DGIM&GI

To gain access to the DGIM&GI, download the following request form for DGIM&GI. Please sign and scan the request form and email to Mr. Min Beom Lee (smin6180@naver.com).

 

Any work that uses this DGIM&GI must acknowledge the authors by including the following reference.

 

Min Beom Lee, Yu Hwan Kim, and Kang Ryoung Park, Conditional Generative Adversarial Network-Based Data Augmentation for Enhancement of Iris Recognition Accuracy, IEEE Access, Vol. 7, pp. 122134-122152, September 2019.

 

< Request Form for DGIM&GI >

 

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39. Dongguk CNN and LSTM models for the classification of multiple gastrointestinal (GI) diseases, and video indices of experimental endoscopic videos

 

(1) Introduction

We trained a cascaded ResNet18 and LSTM model for classification of multiple gastrointestinal diseases by using endoscopic video data. Two different publicly available endoscopic databases [1,2] were considered for the training and validation of our proposed CNN+LSTM based model. Moreover, the trained model is also used in class prediction-based retrieval of endoscopic images. We made our trained model and video indices of experimental endoscopic videos open to other researchers.

 

1. Gastrolab The gastrointestinal site. Available online: http://www.gastrolab.net/ni.htm (accessed on 1 February 2019).

2. Pogorelov, K.; Randel, K. R.; Griwodz, C.; Eskeland, S. L.; de Lange, T.; Johansen, D.; Spampinato, C.; Dang-Nguyen, D.-T.; Lux, M.; Schmidt, P. T.; Riegler, M.; Halvorsen, P. KVASIR: A multi-class image dataset for computer aided gastrointestinal disease detection. In Proceedings of the 8th ACM Multimedia Systems Conference, Taipei, Taiwan, 2023 June 2017; pp. 164169.

 

(2) Request for our CNN+LSTM models and video indices

To gain access to the models and video indices, download the following request form for CNN+LSTM models and video indices. Please sign and scan the request form and email to Mr. Muhammad Owais (malikowais266@gmail.com).

 

Any work that uses these CNN+LSTM models and video indices must acknowledge the authors by including the following reference.

 

Muhammad Owais, Muhammad Arsalan, Jiho Choi, Tahir Mahmood, and Kang Ryoung Park, Artificial Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis, Journal of Clinical Medicine, Vol.  8, Issue 7(986), pp. 1-33, July 2019.

 

< Request Form for CNN+LSTM Models and Video Indices >

 

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38. Dongguk Dual-Camera-based Gaze Database (DDCG-DB1) and

CNN models with Algorithms

 

(1) Introduction

A natural gaze-detection database [Dongguk dual-camera-based gaze database (DDCG-DB1)] is constructed from the images of 26 drivers by dual near-infrared (NIR) light cameras with illuminators in a vehicle environment, and classified into nine situations such as wearing of sunglasses, different glasses, and hats with mobile phones. We make DDCG-DB1 and our CNN model trained with this database open to other researchers.

 

(2) Request for DDCG-DB1 and CNN model

To gain access to the DDCG-DB1 with CNN model, download the following request form. Please scan the request form and email to Mr. Hyo Sik Yoon (yoonhs@dongguk.edu).

Any work that uses or incorporates the dataset must acknowledge the authors by including the following reference.

 

Hyo Sik Yoon, Na Rae Baek, Noi Quang Truong, and Kang Ryoung Park, Driver Gaze Detection Based on Deep Residual Networks Using the Combined Single Image of Dual Near-Infrared Cameras, IEEE Access, Vol. 7, pp. 93448-93461, July 2019.

 

===========================================================================================================================================================================================================

 

< Request Form for DDCG-DB1 and CNN models >

 

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37. Dongguk Mobile Finger-Wrinkle Database (DMFW-DB1) and CNN model with Algorithms

 

(1) Introduction

We collected the smartphone-acquired finger-wrinkle open database DMFW-DB1 using the LG V20s frontal-viewing camera (8 mega-pixels (2,160 × 3,840 pixels), 30 fps, auto-mode) from 33 people (both hands) in five different indoor environments. In addition, we trained finger-wrinkle recognition system based on ResNet-101. We make DMFW-DB1 and our CNN model trained with this database open to other researchers.

 

(2) Request for DMFW-DB1 and CNN model

To gain access to the DMFW-DB1 with CNN model, download the following request form. Please scan the request form and email to Mr. Chan Sik Kim (kimchsi90@dongguk.edu).

Any work that uses or incorporates the dataset must acknowledge the authors by including the following reference.

 

Chan Sik Kim, Nam Sun Cho, and Kang Ryoung Park, Deep Residual Network-Based Recognition of Finger Wrinkles Using Smartphone Camera, IEEE Access, Vol. 7, pp. 71270- 71285, June 2019.

 

===========================================================================================================================================================================================================

 

< Request Form for DMFW-DB1 and CNN models >

 

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36. Dongguk low-resolution drone camera dataset (DLDC-DB1, DLDC-DB2) & CNN models

 

(1) Introduction

We used the Dongguk drone camera dataset ver.2 (DDroneC-DB2) open dataset to make an artificial low-resolution dataset DLDC-DB1 by generating low-resolution images of 80×80 pixels from the original images of 320×320 pixels using bicubic interpolation. Additionally, we collected the real low-resolution dataset DLDC-DB2 using a visible light camera of low-resolution, equipped on a DJI Phantom 4 drone, while landing. The camera presents a downward view of the drone and captures images of 320×240 pixels. We make our CNN models trained by these datasets and open to other researchers, also.

 

(2) Request for DLDC-DB1, DLDC-DB2 and CNN models

To gain access to the datasets with CNN models, download the following request form. Please scan the request form and email to Mr. Noi Quang Truong (noitq.hust@gmail.com).

Any work that uses or incorporates the dataset must acknowledge the authors by including the following reference.

 

Noi Quang Truong, Phong Ha Nguyen, Se Hyun Nam, and Kang Ryoung Park, Deep Learning-Based Super-Resolution Reconstruction and Marker Detection for Drone Landing,IEEE Access, Vol. 7, pp. 61639-61655, May 2019.

 

===========================================================================================================================================================================================================

 

< Request Form for DLDC-DB1, DLDC-DB2 and CNN models >

 

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35. Dongguk CNN Model for CBMIR

 

(1) Introduction

We trained an enhanced ResNet50 model for classification and retrieval of multimodal medical images. 12 different publicly available databases [1] including 50 classes were considered for the training and validation of our enhanced ResNet50. Finally, the trained model is used in content-based medical image retrieval (CBMIR) by performing deep feature-based classification of medical images. We made our trained model open to other researchers.

 

1. Multiple Medical imaging database: Available online: https://sites.google.com/site/aacruzr/image-datasets (accessed on 28 Feb 2019).

 

(2) Request for CNN Model for CBMIR

To gain access to the models, download the following request form for CBMIR-CNN. Please sign and scan the request form and email to Mr. Muhammad Owais (malikowais266@gmail.com).

 

Any work that uses this CNN model must acknowledge the authors by including the following reference.

 

Muhammad Owais, Muhammad Arsalan, Jiho Choi, and Kang Ryoung Park, Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence, Journal of Clinical Medicine, Vol. 8, Issue 4(462), pp. 1-31, April 2019

 

< Request Form for CBMIR-CNN >

 

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34. Dongguk Person ReID CNN Models (DPRID-CNN)

 

(1) Introduction

We trained Person Re-Identification based on ResNet-50 using two databases including Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1) [1] and the Sun Yat-sen University multiple modality Re-ID (SYSU-MM01) finger-vein database [2]. We made trained models open to other researchers.

 

1. DBPerson Recog-DB1. Available in this page No.3.

2. SYSU-MM01. Available online: https://github.com/wuancong/SYSU-MM01 (accessed on 28 Feb 2019).

 

(2) Request for DPRID-CNN

To gain access to the models, download the following request form for DPRID-CNN. Please sign and scan the request form and email to Mr. Jin Kyu Kang (kangjinkyu@dgu.edu).

 

Any work that uses this CNN model must acknowledge the authors by including the following reference.

 

Jin Kyu Kang, Toan Minh Hoang, and Kang Ryoung Park, Person Re-Identification Between Visible and Thermal Camera Images Based on Deep Residual CNN Using Single Input, IEEE Access, Vol. 7, pp. 57972-57984, May 2019.

< Request Form for DPRID-CNN >

 

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33. Dongguk DenseNet-based Finger-vein Recognition Model (DDFRM) with algorithms

 

(1) Introduction

We trained finger-vein recognition system based on DenseNet-161 using two databases including the Hong Kong Polytechnic University Finger Image Database (version 1) [1] and the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger-vein database [2]. We made trained models/Algorithm open to other researchers.

 

1. Kumar, A.; Zhou, Y. Human identification using finger images. IEEE Trans. Image Process. 2012, 21, 2228-2244.

2. SDUMLA-HMT Finger Vein Database. Available online: http://mla.sdu.edu.cn/info/1006/1195.htm (accessed on 7 May 2018).

 

(2) DDFRM with Algorithm Request

To gain access to the models and algorithm, download the following request form for DDFRM with algorithm. Please sign and scan the request form and email to Mr. Jong Min Song (whdwhd93@gmail.com).

 

Any work that uses this CNN model must acknowledge the authors by including the following reference.

 

Jong Min Song, Wan Kim, and Kang Ryoung Park, Finger-vein Recognition Based on Deep DenseNet Using Composite Image, IEEE Access, Vol. 7, pp. 66845- 66863, June 2019.

< Request Form for DDFRM with algorithm >

 

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32. Dongguk OR-Skip-Net Model for Image Segmentation with Algorithm and Black Skin People (BSP) Label Information

 

(1) Introduction

We trained outer skip connection-based deep convolutional network (OR-Skip-net) for image segmentation related to medical diagnosis and other applications, to evaluate the segmentation performance system using ten databases including HGR [1], EDds [2], LIRIS [2], SSG [2], UT [2], AMI [2], Pratheepan [3], BSP, Warwick-QU [4], and NICE.II [9]. We made trained models/Algorithm and BSP label information open to other researchers.

 

1.     Hand detection and pose estimation for creating human-computer interaction project. Available online: http://sun.aei.polsl.pl/~mkawulok/gestures/ip.html (accessed on October 31, 2018).

2.     Skin detection datasets for video monitoring. Available online: http://www-vpu.eps.uam.es/publications/SkinDetDM/ (accessed on November 5, 2018).

3.     Pratheepan dataset + ground truth. Available online: http://cs-chan.com/downloads_skin_dataset.html (accessed on November 5, 2018).

4.     GlaS@MICCAI'2015: Gland segmentation challenge contest. Available online: https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/ (accessed on 24 January 2019).

5.     NICE. II. Noisy iris challenge evaluation - part II. Available online: http://nice2.di.ubi.pt/ (accessed on November 8, 2018).

 

(2) Request for OR-Skip-Net Model with Algorithm and Black Skin People (BSP) Label Information

To gain access to the models, algorithm and BSP label information, download the following request form. Please sign, scan the request form, and email to Mr. Arsalan (arsal@dongguk.edu).

Any work that uses this CNN model with algorithm and label information must acknowledge the authors by including the following reference.

 

Muhammad Arsalan, Dong Seop Kim, Muhammad Owais, and Kang Ryoung Park, OR-Skip-Net: Outer Residual Skip Network for Skin Segmentation in Non-Ideal Situations,Expert Systems With Applications, in press, 2020.

 

< Request form for OR-Skip-Net model with algorithm

and BSP label information >

 

Please complete the following form to request access to the OR-Skip-Net model with algorithm and BSP label information (All contents must be completed). This CNN model with algorithm and label information should not be used for commercial use.

 

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31. Dongguk Banknote Type and Fitness Database (DF-DB3) & CNN Model with algorithms

 

(1) Introduction

We make Dongguk Banknote Type and Fitness Database (DF-DB3) based on Indian rupee (INR) (INR10/20/50/100/500/1000), the Korean won (KRW) (KRW 1000/5000/10000/50000) and United States dollar (USD) (USD 5/10/20/50/100) banknotes, and trained CNN Models of AlexNet, GoogleNet, ResNet-18/50 with algorithms available for the fair comparison by other researchers.

 

(2) Request for Dongguk Banknote Type and Fitness Database (DF-DB3) & CNN Model with algorithms

To gain access to these files, download the following request form. Please scan the request form and email to Dr. Tuyen Danh Pham (phamdanhtuyen@dongguk.edu). Any work that uses these files with algorithm must acknowledge the authors by including the following reference.

 

Tuyen Danh Pham, Dat Tien Nguyen, Chanhum Park and Kang Ryoung Park, Deep Learning-Based Multinational Banknote Type and Fitness Classification with the Combined Images by Visible-Light Reflection and Infrared-Light Transmission Image Sensors, Sensors, Vol. 19, Issue 4(792), pp. 1-28, February 2019.

 

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< Request Form for Dongguk Banknote Type and Fitness Database (DF-DB3) & CNN Model with algorithms >

 

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30. Dongguk RetinaNet for Detecting Road Marking Objects with Algorithms and Annotated Files for Open Databases

 

(1) Introduction

Although the open databases of the Malaga urban dataset [1], the Daimler dataset [2], and the Cambridge dataset [3] have been widely used in previous studies, they do not provide annotated information of road markings. This increases the time and load for system implementation. Therefore, we provide the manually annotated information of road markings for the Malaga urban dataset, the Daimler dataset, and the Cambridge dataset. We also provide the proposed RetinaNet models trained by these databases based on different backbones with and without pre-trained weights to other researchers.

 

1. The Málaga Stereo and Laser Urban Data Set MRPT. Available online: https://www.mrpt.org/MalagaUrbanDataset (accessed on 1 October 2018).

2. Daimler Urban Segmentation Dataset.

Available online: http://www.6d-vision.com/scene-labeling (accessed on 1 October 2018).

3. Cambridge-driving Labeled Video Database (CamVid). Available online: http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/ (accessed on 1 October 2018).

 

(2) Request for Dongguk RetinaNet for Detecting Road Marking Objects with Algorithms and Annotated Files for Open Databases

To gain access to these files, download the following request form. Please scan the request form and email to Mr. Toan Minh Hoang (hoangminhtoan@dongguk.edu). Any work that uses these files with algorithm must acknowledge the authors by including the following reference.

 

Toan Minh Hoang, Phong Ha Nguyen, Noi Quang Truong, Young Won Lee and Kang Ryoung Park, Deep RetinaNet-Based Detection and Classification of Road Markings by Visible Light Camera Sensors, Sensors, Vol. 19, Issue 2(281), pp. 1-25, January 2019.

 

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29. Dongguk CNN Model for NIR Ocular Recognition (DC4NO) with algorithm

 

(1) Introduction

We made our algorithm for rough pupil detection based on sub-block based template matching and deep ResNet models trained with three open databases: CASIA-Iris-Distance, CASIA-Iris-Lamp, and CASIA-Iris-Thousand [1]. We made these trained CNN models for ocular recognition with algorithm open to other researchers.

 

1. CASIA-iris version 4. Available online:

 http://www.cbsr.ia.ac.cn/china/Iris%20Databases%20CH.asp (accessed on 9 November 2018)

 

(2) Request for DC4NO with algorithm

To gain access to the DC4NO with algorithm, download the following request form. Please scan the request form and email to Mr. Young Won Lee (lyw941021@dongguk.edu).

Any work that uses this DC4NO with algorithm must acknowledge the authors by including the following reference.

 

Young Won Lee, Ki Wan Kim, Toan Minh Hoang, Muhammad Arsalan and Kang Ryoung Park, Deep Residual CNN-Based Ocular Recognition Based on Rough Pupil Detection in the Images by NIR Camera Sensor, Sensors, Vol. 19, Issue 4(842), pp. 1-30, February 2019.

 

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28. Dongguk Face Presentation Attack Detection Algorithms by Spatial and Temporal Information (DFPAD-STI)

 

(1) Introduction

We made stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features for face presentation attack detection using the images from CASIA database [1] and Replay-mobile dataset [2], respectively. We made these trained CNN model open to other researchers.

 

1. Zhang, Z.; Yan, J.; Liu, S.; Lei, Z.; Yi, D. Li, S. Z. A face anti-spoofing database with diverse attack. In Proceedings of the 5th International Conference on Biometric, New Delhi, India, 29 March 1 April, 2012.

2. Costa-Pazo, A.; Bhattacharjee, S.; Vazquez-Fernandez, E.; Marcel, S. The replay-mobile face presentation attack database. In Proceedings of the International Conference on the Biometrics Special Interest Group, Darmstadt, Germary, 21-23 September, 2016.

 

(2) Request for DFPAD-STI

To gain access to the DFPAD-STI, download the following request form. Please scan the request form and email to Prof. Dat Tien Nguyen (nguyentiendat@dongguk.edu).

Any work that uses this DFPAD-STI must acknowledge the authors by including the following reference.

 

Dat Tien Nguyen, Tuyen Danh Pham, Min Beom Lee and Kang Ryoung Park, Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information, Sensors, Vol. 19, Issue 2(410), pp. 1-27, January 2019.

 

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27. Dongguk Dual Camera-based Driver Database (DDCD-DB1) and Trained Faster R-CNN Model with Algorithm

 

(1) Introduction

When acquiring DDCD-DB1, the drivers gaze area was divided into 15 zones. The drivers gazed at the 15 zones divided out beforehand in order, and a total of 26 participants were each assigned 8 different situations (i.e., wearing a hat, wearing four different types of glasses (rimless, gold-rimmed, half-frame, and horn-rimmed), wearing sunglasses, making a call through mobile phone, covering face through hand, etc.), and the data were collected by two NIR cameras with NIR illuminators. As the participants gazed at the designated regions in turn, natural head rotations that would occur in actual driving were permitted, and other restrictions or instructions were not provided. When acquiring actual driving data, as there was the risk of a traffic accident, rather than actually driving, a real vehicle (model name of SM5 new impression by Renault Samsung [41]) was started from a parked state in various locations (from daylight road to a parking garage).

In addition, we made two faster R-CNN models trained with our DDCD-DB1 and open database (CAVE-DB [1]), respectively, public.

 

1. Smith, B.A.; Yin, Q.; Feiner, S.K.; Nayar, S.K. Gaze Locking: Passive Eye Contact Detection for Human-Object Interaction. In Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology, St. Andrews, Scotland, UK, 8-11 October 2013; pp. 271280.

 

(2) Request for DDCD-DB1 and faster R-CNN model with algorithms

To gain access to DDCD-DB1 and faster R-CNN model with algorithms, download the following request form. Please scan the request form and email to Mr. Sung Ho Park (pshgod91@dongguk.edu).

Any work that uses this DDCD-DB1 and faster R-CNN model with algorithms must acknowledge the authors by including the following reference.

 

Sung Ho Park, Hyo Sik Yoon and Kang Ryoung Park, Faster R-CNN and Geometric Transformation-Based Detection of Drivers Eyes Using Multiple Near-Infrared Camera Sensors, Sensors, Vol. 19, Issue 1(197), pp. 1-29, January 2019.

 

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< Request Form for DDCD-DB1 and faster R-CNN model with algorithms >

 

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26. Dongguk FRED-Net with Algorithm

 

(1) Introduction

We trained fully residual encoder-decoder network (FRED-Net) CNN models for iris and road scene segmentations, to evaluate the segmentation performance system using seven databases including NICE-II [1], MICHE [2], CASIA distance [3], CASIA interval [3], IITD [4], CamVid [5], and KITTI [6]. We made trained models/Algorithm open to other researchers.

 

6.     NICE.II. Noisy Iris Challenge Evaluation-Part II. Available online: http://nice2.di.ubi.pt/index.html (accessed on 28 December 2017).

7.     De Marsico, M.; Nappi, M.; Riccio, D.; Wechsler, H. Mobile iris challenge evaluation (MICHE)-I, biometric iris dataset and protocols. Pattern Recognit. Lett. 2015, 57, 1723.

8.     CASIA-Iris-databases. Available online: http://biometrics.idealtest.org/dbDetailForUser.do?id=4 (accessed on 28 December 2017).

9.     IIT Delhi Iris Database. Available online: http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm (accessed on 28 December 2017).

10.   Brostow, G. J.; Fauqueur, J.; Cipolla, R. Semantic object classes in video: A high-definition ground truth database. Pattern Recognit. Lett. 2009, 30, 8897.

11.   Geiger, A.; Lenz, P.; Stiller, C.; Urtasun, R. Vision meets robotics: The KITTI dataset. Int. J. Robot. Res. 2013, 32, 12311237.

 

(2) FRED-Net Model with Algorithm Request

To gain access to the models and algorithm, download the following FRED-Net model with algorithm request form. Please sign and scan the request form and email to Mr. Arsalan (arsal@dongguk.edu).

Any work that uses this CNN model must acknowledge the authors by including the following reference.

 

Muhammad Arsalan, Dong Seop Kim, Min Beom Lee, Muhammad Owais, and Kang Ryoung Park, FRED-Net: Fully residual encoderdecoder network for accurate iris segmentation, Expert Systems with Applications, Vol. 122, pp. 217-241, May 2019.

 

 

< FRED-Net model with algorithm Request Form >

 

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25. Dongguk Face and Body Database (DFB-DB1) with CNN models and algorithms

 

(1) Introduction

DFB-DB1 was created for the experiments using images of 22 people obtained by two types of cameras to assess the performance of the proposed method in a variety of camera environments. The first camera was a Logitech BCC 950, and the camera specifications include a camera viewing angle of 78°, a maximum resolution of full high-definition (HD) 1080 p, and auto-focusing at 30 frames per second (fps). The second camera was a Logitech C920, and its specifications include a maximum resolution of full HD 1080p, a viewing angle of 78° at 30 fps, and auto focusing. Images were taken in an indoor environment with indoor lights on, and each camera was installed at a height of 2 m 40 cm. The database was divided into two categories according to the camera. In the first database, the images were captured by the Logitech BCC 950, and the second database is composed of the images obtained by the Logitech C920, and the angle of camera was similar to that for capturing the first database.

In addition, we open our two CNN models trained with DFB-DB1 and open database of ChokePoint database [1], respectively, in addition to our algorithms.

 

1. ChokePoint Database. Available online: http://arma.sourceforge.net/chokepoint/ (accessed on 21 Feb. 2018).

 

(2) Request for DFB-DB1 with CNN model and algorithms

To gain access to DFB-DB1 with CNN model and algorithms, download the following request form. Please scan the request form and email to Mr. Ja Hyung Koo (koo6190@naver.com).

Any work that uses this DFB-DB1 with CNN model and algorithms must acknowledge the authors by including the following reference.

 

Ja Hyung Koo, Se Woon Cho, Na Rae Baek, Min Cheol Kim, and Kang Ryoung Park, CNN-Based Multimodal Human Recognition in Surveillance Environments, Sensors, Vol. 18, Issue 9(3040), pp. 1-34, September 2018. 

 

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< DFB-DB1 and CNN model Request Form >

 

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24. Dongguk Night-Time Face Detection database (DNFD-DB1) and algorithm including CNN model

 

(1) Introduction

DNFD-DB1 is a self-constructed database acquired through a fixed single visible-light camera at a distance of approximately 2022 m at night. The resolution of the camera is 1600 × 1200 pixels, but the image is cropped to the average adult height, which is approximately 600. A total of 2,002 images of 20 different people were prepared, and there are 46 people in each frame. To carry out the 2-fold cross-validation, those 20 people were divided into two subsets of 10 people. In addition, we made two 2-stage Faster R-CNN models trained with our DNFD-DB1 and open database of Fudan University [1], respectively, public.

 

[1] Open database of Fudan University. Available online: https://cv.fudan.edu.cn/_upload/tpl/06/f4/1780/template1780/humandetection.htm (accessed on 26 March 2018).

 

(2) DNFD-DB1 and CNN model Request

To gain access to DNFD-DB1 and CNN model, download the following request form. Please scan the request form and email to Mr. Se Woon Cho (jsu319@naver.com).

Any work that uses this DNFD-DB1 and CNN model must acknowledge the authors by including the following reference.

 

Se Woon Cho, Na Rae Baek, Min Cheol Kim, Ja Hyung Koo, Jong Hyun Kim, and Kang Ryoung Park, Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network, Sensors, Vol. 18, Issue 9(2995), pp. 1-31, September 2018.

 

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< DNFD-DB1 and CNN model Request Form >

 

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23. Dongguk Iris Spoof Detection CNN Model version 2 (DFSD-CNN-2) with Algorithm

 

(1) Introduction

We trained CNN models using local and global image features based on VGG-19-Net architecture for presentation attack detection for iris recognition system using two public databases, Warsaw-2017 [1] and Notre Dame2015 [2], respectively. We made trained models open to other researchers.

 

12.   Yambay, D.; Becker, B.; Kohli, N.; Yadav, D.; Czajka, A.; Bowyer, K. W.; Schuckers, S.; Singh, R.; Vatsa, M.; Noore, A.; Gragnaniello, D.; Sansone, C.; Verdoliva, L.; He, L.; Ru, Y.; Li, H.; Liu, N.; Sun, Z.; Tan, T. LivDet iris 2017 iris liveness detection competition 2017. In Proceedings of the International Conference on Biometrics, Denver, CO, USA, 1-4 October 2017.

13.   Doyle, J. S.; Bowyer, K. W. Robust detection of textured contact lens in iris recognition using BSIF. IEEE Access, 2015, 3, 1672-1683.

 

(2) DFSD-CNN-2 Model Request

To gain access to the models and algorithm, download the following DFSD-CNN-2 request form. Please sign and scan the request form and email to Prof. Nguyen (nguyentiendat@dongguk.edu).

 

Any work that uses this CNN model must acknowledge the authors by including the following reference.

 

D. T. Nguyen, T. D. Pham, Y. W. Lee, and K. R. Park, "Deep Learning-Based Enhanced Presentation Attack Detection for Iris Recognition by Combining Features from Local and Global Regions Based on NIR Camera Sensor," Sensors, Vol. 18, Issue 8(2601), pp. 1-32, August 2018.

 

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< DFSD-CNN-2 model Request Form >

 

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22. Dongguk Fitness Database (DF-DB2) & CNN Model

 

(1) Introduction

 

We collected banknote fitness databases (DF-DB2) from three national currencies, which are Korean won (KRW), Indian rupee (INR), and Unites States dollar (USD). Six denominations exist in the INR dataset: 10, 20, 50, 100, 500, and 1000 rupees, and two denominations exist in the KRW dataset: 1000 and 5000 wons, each of which consists of three fitness levels of fit, normal, and unfit for recirculation, called the case 1 fitness level. In these case 1 datasets, each banknote image was captured using VR sensors on both sides, and IRT sensors on the front side. Five denominations exist for the USD: 5, 10, 20, 50 and 100 dollars, divided into two fitness levels of fit and unfit, called the case 2 fitness level. The number of images captured per banknote was two, including the VR and IRT images of one side of the banknote. In addition, we made CNN models trained with our DF-DB2 public.

 

 

(2) DF-DB2 and CNN model Request

To gain access to DF-DB2 and CNN models, download the following request form. Please scan the request form and email to Prof. Tuyen Danh Pham (phamdanhtuyen@gmail.com).

Any work that uses this DF-DB2 and CNN Model must acknowledge the authors by including the following reference.

 

Tuyen Danh Pham, Dat Tien Nguyen, Jin Kyu Kang, and Kang Ryoung Park, "Deep Learning-Based Multinational Banknote Fitness Classification with a Combination of Visible-Light Reflection and Infrared-Light Transmission Images," Symmetry-Basel, Vol. 10, Issue 10(431), pp. 1-26, October 2018.

 

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21. Dongguk-body-movement-based human identification database version 2 (DBMHI-DB2) & CNN Model

 

(1) Introduction

We have collected our database in both dark and bright environments. The database included both front and back view images of humans. Our database has been collected in five different places in different days with same camera heights. The database consists of data of 100 people including men and women. The database includes both thermal and visible light images but only thermal images have been utilized in this research. The people in our database have different heights and widths, and their sizes vary from 27 to 150 pixels in width and from 90 to 390 pixels in height. In addition, we made our trained CNN and CNN-LSTM model public.

 

(2) DBMHI-DB2 database & the trained CNN model Request

To gain access to the database and CNN model, download the following DBMHI-DB2 and CNN model request form. Please scan the request form and email to Mr. Ganbayar Batchuluun (ganabata87@dongguk.edu).

Any work that uses or incorporates the database and CNN model must acknowledge the authors by including the following reference.

 

Ganbayar Batchuluun, Hyo Sik Yoon, Jin Kyu Kang, and Kang Ryoung Park, "Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network," IEEE Access, Vol. 6, pp. 63164-63186, October 2018.

 

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20. Dongguk Multimodal Recognition CNN of Finger-vein and Finger shape (DMR-CNN) with Algorithm

 

(1) Introduction

We trained multimodal recognition system of finger-vein and finger shape based on ResNet-50 and ResNet-101 using two databases including the Shandong University homologous multi-modal traits (SDUMLA-HMT) [1] and the Hong Kong Polytechnic University Finger Image Database (version 1) [2]. We made trained models/Algorithm open to other researchers.

 

1. SDUMLA-HMT Finger Vein Database. Available online: http://mla.sdu.edu.cn/info/1006/1195.htm (accessed on 7 May 2018).

2. Kumar, A.; Zhou, Y. Human identification using finger images. IEEE Trans. Image Process. 2012, 21, 2228-2244.

 

(2) DMR-CNN Model with Algorithm Request

To gain access to the models and algorithm, download the following DMR-CNN model with algorithm request form. Please sign and scan the request form and email to Mr. Wan Kim (daiz0128@naver.com).

 

Any work that uses this CNN model must acknowledge the authors by including the following reference.

 

W. Kim, J. M. Song, and K. R. Park, Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor, Sensors, Vol. 18, Issue 7(2296), pp. 1-34, July 2018.

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19. Dongguk Drone Camera Database (DDroneC-DB2) with CNN models

 

(1) Introduction

In our experiments, we used a DJI Phantom 4 quadcopter to capture the video while the drone was landing or hovering. It includes a color camera with a 1/2.3-inch-thick complementary metaloxidesemiconductor (CMOS) sensor, with a 94O-field-of-view (FOV) and an f/2.8 lens. The captured videos are in mpeg-4 (MP4) format with 30 fps, and have a size of 1280 x 720 pixels. The drones gimbal is adjusted 90° downward so that during landing, the camera can be facing the ground. In our database (shown in Table 1), we captured three videos, and acquired videos in varying types of environments (humidity level, wind velocity, temperature, and weather). We make our CNN model trained by this database and that trained by PASCAL VOC and Ms COCO databases open to other researchers, also.

 

Table 1. Description of Description of DDroneC-DB2

Sub-dataset

Number of images

Condition

Description

Morning

Far

3088

Humidity: 44.7%

Wind speed: 5.2 m/s

Temperature: 15.2 °C, autumn,sunny

Illuminance:1800 lux

Landing speed: 5.5 m/s

Auto mode of camera shutter speed (8~1/8000 s) and ISO (100~3200)

Close

641

Close

(from DdroneC-DB1)

425

Humidity: 41.5 %

Wind speed: 1.4 m/s

Temperature: 8.6 °C,

spring, sunny

Illuminance: 1900 lux

Landing speed: 4 m/s

Auto mode of camera shutter speed

(8~1/8000 s) and ISO (100~3200)

Afternoon

Far

2140

Humidity: 82.1%

Wind speed: 6.5 m/s

Temperature: 28 °C, summer, sunny

Illuminance:2250 lux

Landing speed: 7 m/s

Auto mode of camera shutter speed (8~1/8000 s) and ISO (100~3200)

Close

352

Close

(from DdroneC-DB1)

148

Humidity: 73.8 %

Wind speed: 2 m/s

Temperature: -2.5 °C,

winter, cloudy

Illuminance: 1200 lux

Landing speed: 6 m/s

Auto mode of camera shutter speed

(8~1/8000 s) and ISO (100~3200)

Evening

Far

3238

Humidity: 31.5%

Wind speed: 7.2 m/s

Temperature: 6.9 °C, autumn, foggy

Illuminance: 650 lux

Landing speed: 6 m/s

Auto mode of camera shutter speed (8~1/8000 s) and ISO (100~3200)

Close

326

Close

(from DdroneC-DB1)

284

Humidity: 38.4 %

Wind speed: 3.5 m/s

Temperature: 3.5 °C,

winter, windy

Illuminance: 500 lux

Landing speed: 4 m/s

Auto mode of camera shutter speed

(8~1/8000 s) and ISO (100~3200)

 

 

(2) Request for DDroneC-DB2 and CNN models

To gain access to the database with CNN models, download the following request form. Please scan the request form and email to Mr. Phong Ha Nguyen (stormwindvn@dongguk.edu).

Any work that uses or incorporates the database must acknowledge the authors by including the following reference.

 

Phong Ha Nguyen, Muhammad Arsalan, Ja Hyung Koo, Rizwan Ali Naqvi, Noi Quang Truong, and Kang Ryoung Park, LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone, Sensors, Vol. 18, Issue 6(1703), pp. 1-30, May 2018.

 

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18. Dongguk Periocular Database (DP-DB1) with CNN models and algorithms

 

(1) Introduction

The DP-DB1 database was created for research on periocular recognition in an indoor surveillance environment. The camera used to capture the images was a Logitech BCC 950, and the specifications of the camera include a camera viewing angle of 79 degrees, a maximum resolution of full high definition (Full HD) 1080p, and a frame rate of 30 fps with auto focusing. The location where the images were captured was an indoor hallway (with indoor lights on), and the camera was installed at a height of 2 m 40 cm. This database consists of 20 people captured in three scenarios: straight line movement, corner movement, and standing still. In case of the standing still scenario, the images were acquired from 4 different positions. In addition, we open our two CNN models trained with DP-DB1 and open database of ChokePoint database [1, 2], respectively, in addition to our algorithms.

 

1. Wong, Y.; Chen, S.; Mau, S.; Sanderson, C.; Lovell, B. C. Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, Colorado Springs, CO, USA, 20-25 June 2011; pp. 74-81.

2. ChokePoint Database. Available online: http://arma.sourceforge.net/chokepoint/ (accessed on 21 Feb. 2018).

 

(2) Request for DP-DB1 with CNN model and algorithms

To gain access to DP-DB1 with CNN model and algorithms, download the following request form. Please scan the request form and email to Mr. Min Cheol Kim (mincheol9166@naver.com).

Any work that uses this DP-DB1 with CNN model and algorithms must acknowledge the authors by including the following reference.

 

Min Cheol Kim, Ja Hyung Koo, Se Woon Cho, Na Rae Baek, and Kang Ryoung Park, Convolutional Neural Network-based Periocular Recognition in Surveillance Environments, IEEE Access, Vol. 6, pp. 57291  57310, October 2018

 

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< Request form for DP-DB1 with CNN model and algorithms >

 

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17. Dongguk IrisDenseNet CNN Model (DI-CNN) with Algorithm

 

(1) Introduction

We trained IrisDenseNet CNN models based on DenseNet and SegNet architecture for iris segmentation, to evaluate the segmentation performance system using five databases including NICE-II [1], MICHE [2], CASIA distance [3], CASIA interval [3] and IITD [4]. We made trained models/Algorithm open to other researchers.

 

14.  NICE.II. Noisy Iris Challenge Evaluation-Part II. Available online: http://nice2.di.ubi.pt/index.html (accessed on 28 December 2017).

15.   De Marsico, M.; Nappi, M.; Riccio, D.; Wechsler, H. Mobile iris challenge evaluation (MICHE)-I, biometric iris dataset and protocols. Pattern Recognit. Lett. 2015, 57, 1723.

16.   CASIA-Iris-databases. Available online: http://biometrics.idealtest.org/dbDetailForUser.do?id=4 (accessed on 28 December 2017).

17.   IIT Delhi Iris Database. Available online: http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm (accessed on 28 December 2017).

 

(2) DI-CNN Model with Algorithm Request

To gain access to the models and algorithm, download the following DI-CNN model with algorithm request form. Please sign and scan the request form and email to Mr. Arsalan (arsal@dongguk.edu).

 

Any work that uses this CNN model must acknowledge the authors by including the following reference.

 

Muhammad Arsalan, Rizwan Ali Naqvi, Dong Seop Kim, Phong Ha Nguyen, Muhammad Owais and Kang Ryoung Park, IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors, Sensors, Vol. 18, Issue 5(1501), pp. 1-30, May 2018.

< DI-CNN model with algorithm Request Form >

 

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16. Dongguk Visible Light Iris Recognition CNN Model (DVLIR-CNN)

 

(1) Introduction

We made the recognition algorithm of iris region by two three convolutional neural networks (CNNs) trained with NICE-II training database [1, 2], the mobile iris challenge evaluation (MICHE) data [3, 4], and CASIA-Iris-Distance database [5], respectively. We made these trained CNN models open to other researchers.

 

18.   NICE.II. Noisy Iris Challenge Evaluation-Part II. Available online: http://nice2.di.ubi.pt/index.html (accessed on 26 July 2017).

19.   Proença, H.; Filipe, S.; Santos, R.; Oliveira, J.; Alexandre, L. A. The UBIRIS.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance.  IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32, 1529-1535.

20.   de Marsico, M.; Nappi, M.; Ricco, D.; Wechsler, H. Mobile iris challenge evaluation (MICHE)-I, biometric iris dataset and protocols. Pattern Recognit. Lett. 2015, 57, 17-23.

21.   Haindl, M.; Krupička, M. Unsupervised detection of non-iris occlusions. Pattern Recognit. Lett. 2015, 57, 60-65.

22.   CASIA-Iris-Distance. Available online: http://biometrics.idealtest.org/dbDetailForUser.do?id=4 (accessed on 13 November 2017).

 

(2) DVLIR-CNN model Request

To gain access to the CNN models, download the following DVLIR-CNN model request form. Please scan the request form and email to Mr. Min Beom Lee (mblee@dongguk.edu).

Any work that uses this CNN Model must acknowledge the authors by including the following reference.

 

Min Beom Lee, Hyung Gil Hong, and Kang Ryoung Park, "Noisy Ocular Recognition Based on Three Convolutional Neural Networks," Sensors, Vol. 17, Issue 12(2933), pp. 1-26, December 2017

 

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15. Dongguk Aggressive and Smooth Driving Database (DASD-DB1) and CNN Model

 

(1) Introduction

We used 15 subjects in the experiment. All the subjects voluntarily participated in our experiments. Because it was too risky to create an aggressive driving situation under real traffic conditions, we utilized two types of driving simulator, to assess baseline aggressive and smooth driving situations. As illustrated in Figure 1, the experiment included 5 min of smooth driving and another 5 min of aggressive driving. Between each section of the experiment, every subject watched a sequence of neutral images from the international affective picture system, thereby maintaining neutral emotional input. After the experiment, the subjects rested for about 10 min. This procedure was repeated three times.

 

(2) DASD-DB1 and CNN model Request

To gain access to DASD-DB1 and CNN models, download the following request form. Please scan the request form and email to Mr. Kwan Woo Lee (leekwanwoo@dgu.edu).

Any work that uses this DASD-DB1 and CNN Model must acknowledge the authors by including the following reference.

 

Kwan Woo Lee, Hyo Sik Yoon, Jong Min Song, and Kang Ryoung Park, Convolutional Neural Network-Based Classification of Drivers Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors,Sensors, Vol. 18, Issue 4(957), pp. 1-22, March 2018

 

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< DASD-DB1 and CNN model Request Form >

 

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14. Dongguk Night-time Pedestrian Detection Faster R-CNN and Algorithm

 

(1) Introduction

 

We made modified faster R-CNN model with algorithm for pedestrian detection at nighttime with the augmented images from KAIST database [1] and Caltech database [2]. We made this trained CNN model open to other researchers.

 

1. Hwang, S.; Park, J.; Kim, N.; Choi, Y.; Kweon, I.S. Multispectral pedestrian detection: Benchmark dataset and baseline. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7-12 June 2015; pp. 1037-1045.

2. Dollár, P.; Wojek, C.; Schiele, B.; Perona, P. Pedestrian detection: An evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 34, 743-761.

 

(2) Modified faster R-CNN model with algorithm Request

To gain access to the CNN models with algorithm, download the following request form. Please scan the request form and email to Mr. Jong Hyun Kim (zzingae@dongguk.edu).

Any work that uses this CNN Model with algorithm must acknowledge the authors by including the following reference.

 

Jong Hyun Kim, Ganbayar Batchuluun, and Kang Ryoung Park, Pedestrian Detection Based on Faster R-CNN in Nighttime by Fusing Deep Convolutional Features of Successive Images, Expert Systems with Applications, (in press).

 

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13. Dongguk Shadow Detection Database (DSDD-DB1) & CNN Model

 

(1) Introduction

 

DSDD-DB1 was obtained by installing visual light cameras 5 to 10 m above the ground, which approximates the conventional height of surveillance camera. As shown in Figure 1 and Table 1, images are shot in the morning, the afternoon, the evening, and on rainy days under various weather conditions, temperature, and illumination. A total of 24,000 images, constituting five sub-datasets, are obtained. The original image size is 800 x 600 pixels of the RGB three-channel.

 

Table 1. Description of five datasets.

Dataset

Condition

Detail Description

I

0.9, afternoon, sunny

humidity 24 %, wind 3.6 m/s

- Shadow with dark color cast due to strong sunlight.

II

6.0, afternoon, cloudy, humidity 39 %, wind 1.9 m/s

- Sunlight weakened by cloud so that a shadow of lighter color is cast.

III

8.0, evening, cloudy, humidity 42 %, wind 3.5 m/s

- Darker image due to weak evening sunlight.

- Long and many shadows due to the sun position in the evening and the reflection on buildings.

IV

5.2, morning, sunny humidity 37 %, wind 0.6 m/s

- Background and object become less distinguishable due to strong morning sunlight.

V

13.8, afternoon, rainy, humidity 65 %, wind 2.0 m/s

- Overall dark image due to rainy day.

- Many shadows generated by wet background floor.

 

In addition, we made two CNN models trained with our DSDD-DB1 and open database (CAVIAR [1]), respectively, public.

 

[1] CAVIAR: Context Aware Vision using Image-based Active Recognition. Available online: http://homepages.inf.ed.ac.uk/rbf/CAVIAR/ (accessed on 8 August 2017).

 

(2) DSDD-DB1 and CNN model Request

To gain access to DSDD-DB1 and CNN models, download the following request form. Please scan the request form and email to Mr. Dong Seop Kim (k_ds1028@naver.com).

Any work that uses this DSDD-DB1 and CNN Model must acknowledge the authors by including the following reference.

 

Dong Seop Kim, Muhammad Arsalan, and Kang Ryoung Park, Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor, Sensors, Vol. 18, Issue 4(960), pp. 1-19, March 2018.

 

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< DSDD-DB1 and CNN model Request Form >

 

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12. Dongguk driver gaze classification database (DDGC-DB1) and CNN model

 

(1) Introduction

 

17 spots (gaze zones) were designated to gaze at for the experiment, and each driver stared at each spot five times. Data were collected from 20 drivers including 3 wearing glasses. The image size is 1600´1200 pixels with 3 channels. When the participants were staring at each spot, they were told to act normally, as if they were actually driving and were not restrained to one position or given any special instructions to act in an unnatural manner. There were risks of car accidents to motivate the participants to accurately stare at the 17 designated spots while actually driving for the experiment. Instead, this study obtained images from various locations (from roads in daylight to a parking garage) in a real vehicle (model name of SM5 New Impression by Renault Samsung) with its power on, but in park to create an environment most similar to when it is being driven (including factors like car vibration and external light). Moreover, to understand the influence of various kinds of external light on driver gaze detection, test data were acquired at different times of the day: in the morning, the afternoon, and at night.

In addition, we made two CNN models trained with our DDGC-DB1 and open database (CAVE-DB [1]), respectively, public.

 

[1] Smith, B.A.; Yin, Q.; Feiner, S.K.; Nayar, S.K. Gaze Locking: Passive Eye Contact Detection for Human-Object Interaction. In Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology, St. Andrews, Scotland, UK, 8-11 October 2013; pp. 271280.

 

(2) DDGC-DB1 and CNN model Request

To gain access to DDGC-DB1 and CNN models, download the following request form. Please scan the request form and email to Mr. Rizwan Ali Naqvi (rizwanali@dongguk.edu).

Any work that uses this DDGC-DB1 and CNN Model must acknowledge the authors by including the following reference.

 

Rizwan Ali Naqvi, Muhammad Arsalan, Ganbayar Batchuluun, Hyo Sik Yoon, and Kang Ryoung Park, Deep Learning-Based Gaze Detection System for Automobile Drivers Using a NIR Camera Sensor, Sensors, Vol. 18, Issue 2(456), pp. 1-34, February 2018.

 

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11. Dongguk Age Estimation CNN Model (DAE-CNN)

 

(1) Introduction

 

We made age estimation robust to optical and motion blurring by Resnet-152 trained with PAL database including artificially (optical and motion) blurred images [1, 2] and MORPH database including artificially (optical and motion) blurred images [3], respectively. We made these trained CNN model open to other researchers.

 

23.   Minear, M.; Park, D.C. A lifespan database of adult facial stimuli. Behav. Res. Methods Instrum. Comput. 2004, 36, 630633.

24.  PAL database. Available online: http://agingmind.utdallas.edu/download-stimuli/face-database/ (accessed on 17 May 2017).

25.   MORPH database. Available online: https://ebill.uncw.edu/C20231_ustores/web/store_main.jsp?STOREID=4 (accessed on 17 May 2017).

 

(2) DAE-CNN model Request

To gain access to the CNN models, download the following DAE-CNN model request form. Please scan the request form and email to Mr. Jeon Seong Kang (kjs2605@dgu.edu).

Any work that uses this CNN Model must acknowledge the authors by including the following reference.

 

Jeon Seong Kang, Chan Sik Kim, Young Won Lee, Se Woon Cho, and Kang Ryoung Park, Age Estimation Robust to Optical and Motion Blurring by Deep Residual CNN, Symmetry-Basel, Vol. 10, Issue 4(108), pp. 1-23, April 2018. 

 

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10. Dongguk Single Camera-based Driver Database (DSCD-DB1)

 

(1) Introduction

 

We collected DSCD-DB1 from a total of 26 participants: 10 wearing nothing, 8 wearing only four kinds of glasses, 5 wearing only two kinds of sunglasses, and 3 wearing only hat. In addition, even the people (wearing nothing) took various pose including putting one hand to cheek or using mobile phone. Fifteen spots in car were designated to gaze at for the experiment, and each participant stared at each spot five times. When the participants were staring at each spot, they were told to act normally, as if they were actually driving and were not restrained to one position or given any special instructions to act in an unnatural manner. There were risks of car accidents and such to motivate the participants to accurately stare at the 15 designated spots while actually driving for the experiment. Instead, this study obtained images from various locations (from roads in daylight to a parking garage) in a real vehicle (model name of SM5 New Impression by Renault Samsung) with its power on, but in park in order to create an environment most similar to when it is being driven (including factors like car vibration and external light). Moreover, to understand the influence of various kinds of external light on driver gaze detection, test data were acquired at different times of the day: in the morning, the afternoon, and at night.

In addition, we collected the data from the participants (sitting at the side seat of driver) gazing at 15 spots while the driver was actually driving the car. Because the data were collected from the participant sitting at the side seat of driver by attaching our gaze tracking device in front of the participant, the conditions of data acquisition were similar to those from driver. In order to check our method in various car environments, we used different car (model name of Daewoo Lacetti Premiere by Chevrolet). The database was collected from a total of 10 participants: 3 wearing nothing, 3 wearing only three kinds of glasses, 2 wearing only two kinds of sunglasses, and 2 wearing only hat. In addition, even the people (wearing nothing) took various pose including putting one hand to cheek or using mobile phone. When the participants were staring at each spot, they were told to act normally, as if they were actually driving and were not restrained to one position or given any special instructions to act in an unnatural manner. To understand the influence of various kinds of external light on driver gaze detection, test data were acquired at different times of the day: in the morning, the afternoon, and at night, and they were collected while driving on various roads.

Data were obtained and processed on a laptop computer with 2.80 GHz CPU (Intel ® Core i5-4200H) and 8 GB of RAM.

 

(2) DSCD-DB1 database Request

To gain access to the database, download the following DSCD-DB1 request form. Please scan the request form and email to Mr. Hyo Sik Yoon (yoonhs@dongguk.edu).

Any work that uses or incorporates the database must acknowledge the authors by including the following reference.

 

Hyo Sik Yoon, Hyung Gil Hong, Dong Eun Lee, and Kang Ryoung Park, Driver's Eye-based Gaze Tracking System by One-Point Calibration, Multimedia Tools and Applications, 2019, in press.

 

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9. ISPR Database (real and presentation attack finger-vein images) & Algorithm Including CNN Model

 

(1) Introduction

The ISPR database consists of 3300 and 7560 images for real and presentation attack finger-vein images, respectively. The real finger-vein database was collected by capturing finger-vein images from 33 people, including both male and female. For each person, all 10 fingers were used and 10 trials were captured for each finger. Consequently, the real finger-vein database contains 3300 (33 people × 10 fingers × 10 trials) real finger-vein images for our experiments. Among 3300 real finger-vein images, we selected 56 fingers which have clear vein pattern for making the presentation attack finger-vein images.

There are two reasons for this selection scheme. First, the users should normally use a finger with clear vein pattern for finger-vein recognition system in order to guarantee the security level of biometric feature. Therefore, if an attacker steals a finger-vein pattern image from user, it will normally be a clear finger-vein image. Second, with some fingers which normally have poor vein pattern such as thumbs or little fingers it is very hard for attackers to produce a clear presentation attack finger-vein image. Instead, the presentation attack finger-vein images can contain poor vein pattern and much of noise due to the reproducing process. As a result, the attacking task can be failed by rejection rate of finger-vein recognition system.

The presentation attack finger-vein image database was collected by re-capturing the printed versions of 56 selected real finger-vein images in three different printing materials including A4 paper, MAT paper and OHP film. In addition, we used three different values of the printing resolution of low resolution (300dpi), middle resolution (1200dpi) and high resolution (2400dpi). By using this scheme, we can collect presentation attack finger-vein images which contain various characteristics about printing materials and printing resolution. Finally, in order to simulate the attacking process, we captured presentation attack finger-vein images at three z-distances (the distance between camera and finger-vein sample) by little changing the z-distance during image acquisition and 5 trials for each z-distance.

As a result, a presentation attack finger-vein image database of 7560 images (56 real image × 3 printing materials × 3 printing resolutions × 3 z-distances × 5 trials) was collected.

 

Table 1. Description of ISPR presentation attack finger-vein image database

Image Making Protocol

Real Access

Printed Access

Train Set

Test Set

Total

Train Set

Test Set

Total

Material

Printed on A4 Paper

(ISPR-DB1)

1700

1600

3300

1440

1080

2520

Printed on MAT Paper

(ISPR-DB2)

1700

1600

3300

1440

1080

2520

Printed on OHP Film

(ISPR-DB3)

1700

1600

3300

1440

1080

2520

Printer Resolution

Printed using 300 DPI Resolution Printer

(ISPR-DB4)

1700

1600

3300

1440

1080

2520

Printed using 1200 DPI Resolution Printer. (ISPR-DB5)

1700

1600

3300

1440

1080

2520

Printed using 2400 DPI Resolution Printer

(ISPR-DB6)

1700

1600

3300

1440

1080

2520

Entire Database (ISPR-DB)

1700

1600

3300

4320

3120

7560

 

In addition, we made our algorithm including the trained CNN model public.

 

(2) ISPR database & algorithm including the trained CNN model Request

To gain access to the database and algorithm, download the following ISPR database and algorithm request form. Please scan the request form and email to Prof. Dat Tien Nguyen (nguyentiendat@dongguk.edu).

Any work that uses or incorporates the database and algorithm must acknowledge the authors by including the following reference.

 

Dat Tien Nguyen, Hyo Sik Yoon, Tuyen Danh Pham, and Kang Ryoung Park, "Spoof Detection for Finger-Vein Recognition System Using NIR Camera," Sensors, Vol. 17, Issue 10(2261), pp. 1-33, October 2017.

 

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===========================================================================================================================================================================================================

 

 

 

8. Dongguk Visible Light & FIR Pedestrian Detection Database (DVLFPD-DB1) && CNN model

 

(1) Introduction

 

There are 4 sub-databases, and the total number of frames of visible light images and FIR images is 4080 each. To obtain the images, this study used a duel camera system consisting of a FLIR Tau640 FIR camera (19 mm), and a Logitech C600 visible light web-camera. In order to record the filming conditions, a WH-1091 (wireless weather station) was used.

 

Table 1. Description of Database

 

Sub-database

1

Sub-database

2

Sub-database

3

Sub-database

4

Number of

image

598

651

2364

467

Number of      pedestrian candidate

1123

566

2432

169

Number of           non-pedestrian candidate

763

734

784

347

(range of width) ´ (range of height)

(pixels)

Pedestrian

(27 ~ 91) ´     (87 ~ 231)

(47 ~ 85) ´   (85 ~ 163)

(31 ~ 105) ´  (79 ~ 245)

(30 ~ 40) ´   (90 ~ 120)

Non-pedestrian

(51 ~ 161) ´   (63 ~ 142)

(29 ~ 93) ´   (49 ~ 143)

(53 ~ 83) ´  (55 ~ 147)

(60 ~ 170) ´  (50 ~ 110)

Weather Conditions

Surface Temperature: 30.4°C,

Air Temperature: 22.5°C  

Wind speed: 10 km/h   Sensory Temperature: 21.3°C

Surface Temperature: 25.5°C

Air Temperature: 24 Wind speed: 5 km/h Sensory Temperature: 23.5°C

Surface Temperature: 20°C

Air Temperature: 21°