< Dongguk Open
Databases & CNN Model >
---------------------------------------------
35. Dongguk FRED-Net with Algorithm
34. Dongguk Face and Body Database (DFB-DB1)
with CNN models and algorithms
33. Dongguk Night-Time Face Detection
database (DNFD-DB1) and algorithm including CNN model
32. Dongguk Iris Spoof Detection CNN Model
version 2 (DFSD-CNN-2) with Algorithm
31. Dongguk Fitness Database
(DF-DB2) & CNN Model
30.Dongguk-body-movement-based human
identification database version 2 (DBMHI-DB2) & CNN Model
29. Dongguk Multimodal Recognition CNN of
Finger-vein and Finger shape (DMR-CNN) with Algorithm
28. Dongguk Drone Camera Database
(DDroneC-DB2) with CNN models
27. Dongguk Periocular Database (DP-DB1) with
CNN models and algorithms
26. Dongguk IrisDenseNet
CNN Model (DI-CNN) with Algorithm
25. Dongguk Iris Spoof Detection CNN Model
(DISD-CNN) with Algorithm
24. Dongguk Visible Light
Iris Recognition CNN Model (DVLIR-CNN)
23. Dongguk Aggressive and
Smooth Driving Database (DASD-DB1)
and CNN Model
22. Dongguk Night-time
Pedestrian Detection Faster R-CNN and
Algorithm
21. Dongguk Face Spoof
Detection CNN Model (DFSD-CNN) with
Algorithm
20. Dongguk Shadow Detection
Database (DSDD-DB1) & CNN
Model
19. Dongguk Fitness Database
(DF-DB1) & CNN Model
18. Dongguk driver gaze
classification database (DDGC-DB1) and
CNN
model
17. Dongguk Age Estimation
CNN Model (DAE-CNN)
16. Dongguk Single
Camera-based Driver Database (DSCD-DB1)
15. Dongguk Body
Movement-based Human Identification Database
(DBMHI-DB1)
& CNN Model
14. Dongguk Visible Light
Iris Segmentation CNN Model (DVLIS-CNN)
13. Dongguk Drone Camera
Database (DDroneC-DB1)
12. ISPR Database (real and presentation attack
finger-vein images)
&
Algorithm Including CNN Model
11. Dongguk Visible Light
& FIR Pedestrian Detection Database
(DVLFPD-DB1)
& CNN Model
10. Dongguk Open and Closed
Eyes Database (DOCE-DB1) & CNN
Model
9. Dongguk Multi-national
Currencies Database (DMC-DB1) & CNN
Model
8. Dongguk Finger-Vein
Database (DFingerVein-DB1) & CNN Model
7. Dongguk Night-time Human
Detection Database (DNHD-DB1) &
CNN
Model
6. Dongguk Body-based Person
Recognition Database (DBPerson-
Recog-DB1)
5. Dongguk
Body-based Gender Database (DBGender-DB2)
4. Dongguk
Activities & Actions Database (DA&A-DB1)
3. Dongguk
Body-based Gender Database (DBGender-DB1)
2. Dongguk
Face Database (DFace-DB1)
1. Dongguk
Banknote Database (DBanknote-DB1)
35. 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.
1.
NICE.II. Noisy Iris Challenge
Evaluation-Part II. Available online: http://nice2.di.ubi.pt/index.html
(accessed on 28 December 2017).
2.
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, 17–23.
3.
CASIA-Iris-databases.
Available online: http://biometrics.idealtest.org/dbDetailForUser.do?id=4
(accessed on 28 December 2017).
4.
IIT Delhi Iris Database.
Available online:
http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm (accessed on 28
December 2017).
5.
Brostow, G. J.; Fauqueur, J.; Cipolla,
R. Semantic object classes in video: A high-definition ground truth database.
Pattern Recognit. Lett. 2009, 30, 88–97.
6.
Geiger, A.; Lenz, P.; Stiller,
C.; Urtasun, R. Vision meets robotics: The KITTI
dataset. Int. J. Robot. Res. 2013, 32, 1231–1237.
(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 Encoder-Decoder Network for Accurate Iris Segmentation,” Expert Systems with Applications, in submission.
<
FRED-Net model with algorithm Request Form >
Please complete the following form to request access to the FRED-Net model with algorithm (All contents must be completed). This CNN model with algorithm should not be used for commercial use.
Name:
Contact: (Email)
(Telephone)
Organization Name:
Organization Address:
Purpose:
Date:
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34. 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.
===========================================================================================================================================================================================================
<
DFB-DB1 and CNN model Request Form >
Please complete the following form to request access to the DFB-DB1 and CNN model (All contents must be completed). This database and CNN model should not be used for commercial use.
Name :
Contact : (Email)
(Telephone)
Organization Name :
Organization Address :
Purpose :
Date :
Name (signature)
33. 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 20–22 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 4–6 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.
===========================================================================================================================================================================================================
<
DNFD-DB1 and CNN model Request Form >
Please complete the following form to request access to the DNFD-DB1 and CNN model (All contents must be completed). This database and CNN model should not be used for commercial use.
Name :
Contact : (Email)
(Telephone)
Organization Name :
Organization Address :
Purpose :
Date :
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32. 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 Dame–2015 [2], respectively. We made trained models open to other researchers.
7. 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.
8. 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 Local and Global Features Based on NIR Camera Sensor,” Sensors, in submission, 2018.
===========================================================================================================================================================================================================
<
DFSD-CNN-2 model Request Form >
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(Telephone)
Organization Name:
Organization Address:
Purpose:
Date:
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31. 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.
===========================================================================================================================================================================================================
<
DF-DB2 and CNN model Request Form >
Please complete the following form to request access to the DF-DB2 and CNN model (All contents must be completed). This database and CNN model should not be used for commercial use.
Name :
Contact : (Email)
(Telephone)
Organization Name :
Organization Address :
Purpose :
Date :
Name (signature)
30. 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, in submission.
===========================================================================================================================================================================================================
<
DBMHI-DB2 database & the trained CNN model Request
Form >
Please complete the following form to request access to the DBMHI-DB2 and CNN model (All contents must be completed). This database and CNN model should not be used for commercial use.
Name :
Contact : (Email)
(Telephone)
Organization Name :
Organization Address :
Purpose :
Date :
Name (signature)
===========================================================================================================================================================================================================
29. 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.
<
DMR-CNN model with algorithm Request Form >
Please complete the following form to request access to the DMR-CNN model with algorithm (All contents must be completed). This CNN model with algorithm should not be used for commercial use.
Name:
Contact: (Email)
(Telephone)
Organization Name:
Organization Address:
Purpose:
Date:
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28. 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 metal–oxide–semiconductor (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 drone’s 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.
===========================================================================================================================================================================================================
<
Request Form for DDroneC-DB2 and CNN models >
Please complete the following form to request access to the DDroneC-DB2 and CNN models (All contents must be completed). This database should not be used for commercial use.
Name :
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(Telephone)
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27. 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.
===========================================================================================================================================================================================================
<
Request form for DP-DB1 with CNN model and
algorithms >
Please complete the following form to request access to the DP-DB1 with CNN model and algorithms (All contents must be completed). This database and CNN model with algorithms should not be used for commercial use.
Name :
Contact : (Email)
(Telephone)
Organization Name :
Organization Address :
Purpose :
Date :
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26. 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.
9. NICE.II. Noisy Iris Challenge Evaluation-Part II. Available online:
http://nice2.di.ubi.pt/index.html (accessed on 28 December 2017).
10. 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, 17–23.
11. CASIA-Iris-databases. Available online:
http://biometrics.idealtest.org/dbDetailForUser.do?id=4 (accessed on 28
December 2017).
12. 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 >
Please complete the following form to request access to the DI-CNN model with algorithm (All contents must be completed). This CNN model with algorithm should not be used for commercial use.
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25. Dongguk Iris Spoof Detection CNN Model
(DISD-CNN) with Algorithm
(1) Introduction
We trained CNN models based on VGG-19-Net architecture for presentation attack detection for iris recognition system using two public databases, Warsaw-2017 [1] and Notre Dame – 2015 [2], as shown in Tables 1 and 2. We made trained models open to other researchers.
Table 1. Description of training and testing data used
with Warsaw-2017 dataset
|
Dataset |
Training dataset |
Testing dataset |
|||||||
|
Real image |
Attack image |
Total |
Test-known dataset |
Test-unknown
dataset |
|||||
|
Real image |
Attack image |
Total |
Real image |
Attack image |
Total |
||||
|
Original dataset |
1,844 |
2,669 |
4,513 |
974 |
2,016 |
2,990 |
2,350 |
2,160 |
4,510 |
|
Augmented dataset |
27,660 (1,844ⅹ15) |
24,021 (2,669ｘ9) |
51,681 |
974 |
2,016 |
2,990 |
2,350 |
2,160 |
4,510 |
Table 2. Description of training and testing data used
with Notre Dame 2015 dataset
|
Dataset |
Training dataset |
Testing dataset |
|||||||
|
Real image |
Attack image |
Total |
Test-known dataset |
Test-unknown
dataset |
|||||
|
Real image |
Attack image |
Total |
Real image |
Attack image |
Total |
||||
|
Original ND2015
dataset |
600 |
600 |
1,200 |
900 |
900 |
1,800 |
900 |
900 |
1,800 |
|
Augmented dataset |
29,400 (600ⅹ49) |
29,400 (600ｘ49) |
58,800 |
900 |
900 |
1,800 |
900 |
900 |
1,800 |
13. 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.
14. Doyle, J. S.; Bowyer, K. W. Robust detection of textured contact lens in
iris recognition using BSIF. IEEE Access, 2015, 3, 1672-1683.
(2) DISD-CNN Model Request
To gain access to the models and algorithm, download the following DISD-CNN request form. Please sign and scan the request form and email to Mr. Nguyen (nguyentiendat@dongguk.edu).
Any work that uses this CNN model must acknowledge the authors by including the following reference.
Dat Tien
Nguyen, Na Rae Baek, Tuyen Danh Pham, and Kang Ryoung Park, “Presentation Attack Detection for Iris Recognition
System Using NIR Camera Sensor,” Sensors, Vol. 18,
Issue 5(1315), pp. 1-30, May 2018.
<
DISD-CNN model Request Form >
Please complete the following form to request access to the DISD-CNN model (All contents must be completed). This CNN model should not be used for commercial use.
Name:
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24. 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.
15. NICE.II. Noisy Iris
Challenge Evaluation-Part II. Available online: http://nice2.di.ubi.pt/index.html
(accessed on 26 July 2017).
16.
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.
17.
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.
18.
Haindl,
M.; Krupička, M. Unsupervised detection of
non-iris occlusions. Pattern Recognit.
Lett. 2015,
57, 60-65.
19. 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.
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23. 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.
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22. 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.
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faster R-CNN model with algorithm Request Form
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21. Dongguk Face Spoof Detection CNN Model
(DFSD-CNN) with Algorithm
(1) Introduction
We made CNN model with algorithm for face spoof detection with the augmented images from NUAA database [1] and CASIA database [2], respectively. We made these trained CNN model open to other researchers.
20. Tan, X.;
Li, Y.; Liu, J.; Jiang, L. Face liveness detection from a single image with
sparse low rank bilinear discriminative model. In Proceedings of the 11th
European Conference on Computer Vision, Greece, 2010.
21.
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) DFSD-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 Prof. Dat Tien Nguyen (nguyentiendat@dongguk.edu).
Any work that uses this CNN Model with algorithm must acknowledge the authors by including the following reference.
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20. 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.
===========================================================================================================================================================================================================
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19. Dongguk Fitness Database (DF-DB1)
& CNN Model
(1) Introduction
We collected banknote
fitness databases (DF-DB1) from three national currencies, which are Korean won (KRW), Indian rupee (INR), and Unites
States dollar (USD)
as shown in Table 1. The KRW banknote image database is composed of banknotes
in two denominations, 1000 and 5000 wons. The
denominations of banknotes in the INR database are 10, 20, 50,100, 500, and
1000 rupees. Those for the case of the USD are 5, 10, 50, and 100 dollars.
Three levels of fitness, which are fit, normal, and unfit for recirculation,
are assigned for the banknotes of each denomination in the cases of the KRW and
INR, and two levels including fit and unfit are defined for the USD banknotes
in the experimental dataset.
Table 1. Number of banknote images in each national
currency database.
|
Fitness Levels |
KRW |
INR |
USD |
|
|
Fit |
Number of Images |
10,084 |
11,909 |
2,907 |
|
Number of Images after Data
Augmentation |
30,252 |
71,454 |
61,047 |
|
|
Normal |
Number of Images |
12,430 |
7,952 |
N/A |
|
Number of Images after Data
Augmentation |
37,290 |
47,712 |
N/A |
|
|
Unfit |
Number of Images |
11,274 |
2,203 |
642 |
|
Number of Images after Data
Augmentation |
33,822 |
13,218 |
45,582 |
|
In addition, we made CNN
models trained with our DF-DB1 public.
(2) DF-DB1 and CNN model Request
To gain access to DF-DB1 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-DB1 and CNN Model must acknowledge the authors by including the following reference.
===========================================================================================================================================================================================================
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18. 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. 271–280.
(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.
===========================================================================================================================================================================================================
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17. 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.
22. Minear, M.;
Park, D.C. A lifespan database of adult facial stimuli. Behav.
Res. Methods Instrum. Comput. 2004,
36, 630–633.
23.
PAL database. Available
online: http://agingmind.utdallas.edu/download-stimuli/face-database/ (accessed
on 17 May 2017).
24. 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.
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16. 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 Based on Single NIR Camera," in submission to Multimedia Tools and Applications
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===========================================================================================================================================================================================================
15. Dongguk Body Movement-based Human
Identification Database (DBMHI-DB1) & 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 80 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. The description of this database
is presented in Table 1.
Table 1. Description of our
database
|
Datasets |
Condition |
Detailed description |
|
I |
19 °C, 62 lux |
-
The 3rd
floor inside building -
A man is walking in a
corridor -
Halo effect occurs
below feet in thermal image |
|
II |
17 °C, 84 lux |
-
The 6th
floor inside building -
Four men are walking in
a corridor -
Halo effect occurs
below feet in thermal image |
|
III |
20 °C, 125 lux |
-
The 7th
floor inside building -
A man is walking in a
corridor while using a cellphone -
Difference between
human body and background is small |
|
18 °C, 8 lux |
-
The 7th
floor inside building -
A woman with a bag is
walking in a dark corridor -
Halo effect occurs
below feet in thermal image |
|
|
IV |
18 °C, 140 lux |
-
The 8th
floor inside building -
A man is walking in a
corridor -
Halo effect occurs
below feet in thermal image |
|
17 °C, 1 lux |
-
The 8th
floor inside building -
Two men are walking in
a dark corridor -
Halo effect occurs
below feet in thermal image |
|
|
V |
18 °C, 130 lux |
-
The 7th
floor inside building (different corridor than that in dataset III) -
Two men are walking in
a corridor -
Difference between
human body and background is small |
|
18 °C, 1 lux |
-
The 7th floor
inside building (different corridor than that in dataset III) -
A man with a small item
is walking in a dark corridor -
Halo effect occurs
below feet in thermal image |
In addition, we made our
trained CNN model public.
(2) DBMHI-DB1 database & the trained CNN model Request
To gain access to the database and CNN model, download the following DBMHI-DB1 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, Rizwan Ali Naqvi, Wan Kim, and Kang Ryoung Park, "Body-movement-based human identification using convolutional neural network," Expert Systems with Applications, Vol. 101, pp. 56-77, July 2018.
===========================================================================================================================================================================================================
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===========================================================================================================================================================================================================
14. Dongguk Visible Light Iris
Segmentation CNN Model (DVLIS-CNN)
We made the segmentation algorithm of iris region by two convolutional neural networks (CNNs) trained with NICE-II training database [1, 2] and the mobile iris challenge evaluation (MICHE) data [3, 4], respectively. We made these trained CNN models open to other researchers.
25. NICE.II. Noisy Iris
Challenge Evaluation-Part II. Available online: http://nice2.di.ubi.pt/index.html
(accessed on 26 July 2017).
26.
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.
27.
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.
28.
Haindl,
M.; Krupička, M. Unsupervised detection of
non-iris occlusions. Pattern Recognit.
Lett. 2015,
57, 60-65.
(2) DVLIS-CNN model Request
To gain access to the CNN models, download the following DVLIS-CNN model request form. Please scan the request form and email to Mr. Muhammad Arsalan (arsal@dongguk.edu).
Any work that uses this CNN Model must acknowledge the authors by including the following reference.
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Contact : (Email)
(Telephone)
Organization Name :
Organization Address :
Purpose :
Date :
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13. Dongguk Drone Camera Database
(DDroneC-DB1)
(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 metal–oxide–semiconductor (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 drone’s gimbal is adjusted 90° downward so that during landing, the camera can be facing the ground. Our database (shown in Table 1) is divided in two sub databases: drone landing on the marker and drone hovering over the same position while the marker is moving on the ground. For each sub database, we captured four videos at 10 AM, 2 PM, 6 PM, and 10 PM. We acquired videos in varying types of environments (humidity level, wind velocity, temperature, and weather). The marker was visible in the sequences for the morning, afternoon, and evening, but it was barely seen in the night video.
Table 1. Description of Description of DDroneC-DB1
|
Kinds of sub-database |
Time |
Condition |
Description |
|
Sub-database 1 (drone landing) |
Morning |
Humidity: 41.5 %, wind speed: 1.4 m/s, temperature: 8.6 oC, spring,
sunny |
- A sunny day with clear sky, which has affected the illumination on the marker - Landing speed: 4 m/s |
|
Afternoon |
Humidity: 73.8 %, wind speed: 2 m/s, temperature: -2.5 oC, winter,
cloudy |
- Low level of illumination
observed in the winter time, which affected the intensity of background area. - Landing speed: 6 m/s |
|
|
Evening |
Humidity: 38.4 %, wind speed: 3.5 m/s, temperature: 3.5 oC, winter,
windy |
- There is the change in the marker’s position due to strong wind - Landing speed: 4 m/s |
|
|
Night |
Humidity: 37.5 %, wind speed: 3.2 m/s, temperature: 6.9 oC, spring, foggy |
- Marker cannot be seen owning
low level of light at dark night - Landing speed: 6 m/s |
|
|
Sub-database 2 (drone hovering) |
Morning |
Humidity: 41.6 %, wind speed: 2.5 m/s, temperature: 11 oC, spring, foggy |
Drone hovers above the marker, and the marker is manually
moved and rotated while capturing videos |
|
Afternoon |
Humidity: 43.5 %, wind speed: 2.8 m/s, temperature: 13 oC, spring, sunny |
||
|
Evening |
Humidity: 42.9 %, wind speed: 2.9 m/s, temperature: 10 oC, spring, sunny |
||
|
Night |
Humidity: 41.5 %, wind speed: 3.1 m/s, temperature: 6oC, spring, dark
night |
(2) DDroneC-DB1 Request
To gain access to the database, download the following DDroneC-DB1 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, Ki Wan Kim, Young Won Lee, and Kang Ryoung Park, "Remote Marker-Based Tracking for UAV
Landing Using Visible-Light Camera Sensor," Sensors, Vol. 17, Issue 9(1987), pp.
1-38, August 2017
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12. 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.
===========================================================================================================================================================================================================
<
ISPR Database & Algorithm Request Form >
Please complete the following form to request access to the ISPR database and algorithm (All contents must be completed). This database and algorithm should not be used for commercial use.
Name :
Contact : (Email)
(Telephone)
Organization Name :
Organization Address :
Purpose :
Date :
Name (signature)
===========================================================================================================================================================================================================
11. 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) |
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