< Dongguk Open Databases >

 

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1.  Dongguk Drone Camera Database (DDroneC-DB1)

2.  Dongguk Visible Light & FIR Pedestrian Detection Database (DVLFPD-DB1) & CNN Model

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

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

5.  Dongguk Finger-Vein Database (DFingerVein-DB1) & CNN Model

6.  ISPR database (real and presentation attack finger-vein images)

7.  Dongguk Night-time Human Detection Database (DNHD-DB1) & CNN Model

8.  Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)

9.  Dongguk Body-based Gender Database (DBGender-DB2)

10.    Dongguk Activities & Actions Database (DA&A-DB1)

11.    Dongguk Body-based Gender Database (DBGender-DB1)

12.    Dongguk Face Database (DFace-DB1)

13.    Dongguk Banknote Database (DBanknote-DB1)

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1. 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, Yong Won Lee and Kang Ryoung Park, " Remote Marker-based Tracking for UAV Landing Using Visible-Light Camera Sensor," in submission to Remote Sensing

 

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< DDroneC-DB1 Request Form >

 

Please complete the following form to request access to the DDroneC-DB1 (All contents must be completed). This database should not be used for commercial use.

 

Name :

 

Contact : (Email)

(Telephone)

 

Organization Name :

 

Organization Address :

 

Purpose :

 

 

Date :

 

               Name (signature)

 

 

2. 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°C     Wind Speed: 6.1 km/h   Sensory Temperature: 21°C	Surface Temperature: 16°C Air Temperature: 20.5°C    Wind Speed: 2.5 km/h   Sensory Temperature: 20.8°C

 

In addition, we obtained the artificially degraded database by Gaussian noise and Gaussian blurring with the original dataset of Table 1, and make this degraded dataset public, also.

 

Also, we made the classification algorithm of pedestrian and non-pedestrian by convolutional neural network (CNN) trained with DVLFPD-DB1. We also made this trained CNN model open to other researchers.

 

 

(2) DVLFPD-DB1 & CNN model Request

To gain access to the database and CNN models, download the following DVLFPD-DB1 & CNN model request form. Please scan the request form and email to Mr. Jin Kyu Kang (kangjinkyu@dgu.edu).

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

 

Jin Kyu Kang, Hyung Gil Hong, and Kang Ryoung Park, "Pedestrian Detection Based on Adaptive Selection of Visible Light or FIR Light Camera Image by Fuzzy Inference System and CNN-Based Verification," in submission to Sensors

 

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< DVLFPD-DB1 & CNN model Request Form >

 

Please complete the following form to request access to the DVLFPD-DB1 & 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)

 

 

 

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

 

(1) Introduction

 

DOCE-DB1 was created in an environment with a person watching TV indoors, with images taken at distances between 2 and 2.5 m. The camera used for capturing the images was a Logitech C600 web camera with a zoom lens attached. We used eye images obtained by extracting facial and eye features from images captured at a resolution of 1600 × 1200 pixels. Table 1 shows the descriptions of original and augmented DOCE-DB1.

 

Table 1. Descriptions of original and augmented databases

	Original database	Augmented database
Open eye images	2,062	103,100
Closed eye images	4,763	238,150
Total	6,825	341,250

 

In addition, we made the classification algorithm of open and closed eye by convolutional neural network (CNN) trained with DOCE-DB1. We also made this trained CNN model with other various (trained) CNN models (based on AlexNet, VGG face, GoogLeNet) open to other researchers.

 

 

(2) DOCE-DB1 & CNN model Request

To gain access to the database and CNN models, download the following DOCE-DB1 & CNN model request form. Please scan the request form and email to Mr. Ki Wan Kim (yawara18@dgu.edu).

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

 

Ki Wan Kim, Hyung Gil Hong, Gi Pyo Nam, and Kang Ryoung Park, "Deep Residual Network-based Classification of Open and Closed Eyes using Visible Light Camera Sensor," in submission to Sensors

 

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< DOCE-DB1 & CNN model Request Form >

 

Please complete the following form to request access to the DOCE-DB1 & 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)

 

 

 

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

 

(1) Introduction

Our multi-national banknote image database (DMC-DB1) containing images from six national currencies, which are CNY, EUR, JPY, KRW, RUB and USD. There were 64,668 images captured in both sides of 32,334 banknotes belonging to 62 denominations of currencies from six countries. The number of classes is four times of the number of denominations because of the inclusion of four directions; therefore, there are totally 248 classes of banknote to be classified in our study. In Table 1, we give the details numbers of images and classes (denominations and directions) of each country’s banknote in the dataset.

 

Table 1. Number of images and classes in our multi-national banknote database

Currency	Number of Images	Number of Classes
CNY	626	40
EUR	4,324	44
JPY	1,462	28
KRW	536	28
RUB	12,146	40
USD	45,574	68

 

In addition, we made the classification algorithm of multi-national currencies by convolutional neural network (CNN) trained with DMC-DB1. We also made this trained CNN model open to other researchers.

 

 

(2) DMC-DB1 & CNN model Request

To gain access to the database and CNN model, download the following DMC-DB1 & CNN model request form. Please scan the request form and email to Prof. Tuyen Danh Pham (phamdanhtuyen@gmail.com).

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

 

Tuyen Danh Pham, Dong Eun Lee, and Kang Ryoung Park, "Multi-National Banknote Classification based on Visible-light Line Sensor and Convolutional Neural Network," in submission to Sensors

 

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< DMC-DB1 & CNN model Request Form >

 

Please complete the following form to request access to the DMC-DB1 & 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)

 

 

 

5. Dongguk Finger-Vein Database (DFingerVein-DB1) & CNN Model

(1) Introduction

DFingerVein-DB1 includes two databases. The first database (good-quality database) was acquired with two guide bars (fingertip guide bar and finger side guide bar) attached, when the images were captured using the finger-vein capturing device made by our lab. Ten images each were captured from the index, middle, and ring fingers of the left and right hands of 20 persons. Thus, this database has a total of 1,200 images consisting of 20 people × 2 hands × 3 fingers × 10 images. This is called good-quality database, because the images have few finger-vein misalignments by finger translation owing to the two guide bars, and there is little shading in the finger-vein images.

For the second database (mid-quality database), the two guide bars were removed when the images were captured with the same device used for the collection of good-quality database. Ten images each were captured from the index, middle, and ring fingers of the left and right hands of 33 persons. Thus, this database has a total of 1,980 images consisting of 33 people × 2 hands × 3 fingers × 10 images. This is called mid-quality database because the images were captured with no guide bar, and the images have finger-vein misalignments by finger translation; however, there is little shading in the finger-vein images similar to the good-quality database.

 

Table 1. Descriptions of the collected databases

 

In addition, we made the finger-vein recognition algorithm by convolutional neural network (CNN) trained with DFingerVein-DB1. We also made this trained CNN model open to other researchers.

 

 

(2) DFingerVein-DB1 & CNN Model Request

To gain access to the database & CNN Model, download the following DFingerVein-DB1 & CNN Model request form. Please scan the request form and email to Mr. Hyung Gil Hong (hell@dongguk.edu).

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

 

Hyung Gil Hong, Min Beom Lee, and Kang Ryoung Park, "Convolutional Neural Network-based Finger-vein Recognition Using NIR Image Sensor," in submission to Sensors

 

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< DFingerVein-DB1 & CNN Model Request Form >

 

Please complete the following form to request access to the DFingerVein-DB1 & CNN Model (All contents must be completed). This database & CNN Model should not be used for commercial use.

 

Name :

 

Contact : (Email)

(Telephone)

 

Organization Name :

 

Organization Address :

 

Purpose :

 

 

Date :

 

               Name (signature)

 

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6. ISPR database (real and presentation attack finger-vein images)

 

(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

 

(2) ISPR database Request

To gain access to the database, download the following ISPR database 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 must acknowledge the authors by including the following reference.

 

Dat Tien Nguyen, Hyo Sik Yoon and Kang Ryoung Park, "Presentation Attack Detection for Finger-vein Biometric System Based on Convolutional Neural Network and Support Vector Machine," in submission to Sensors

 

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< ISPR database Request Form >

 

Please complete the following form to request access to the ISPR database (All contents must be completed). This database should not be used for commercial use.

 

Name :

 

Contact : (Email)

(Telephone)

 

Organization Name :

 

Organization Address :

 

Purpose :

 

 

Date :

 

               Name (signature)

 

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

 

 

7. Dongguk Night-time Human Detection Database (DNHD-DB1) & CNN model

 

(1) Introduction

This database was obtained through a nighttime visible light camera (Logitech C600 web camera (https://support.logitech.com/en_us/product/5869)). DNHD-DB1 was constructed using images obtained from cameras fixed at various locations.

 

Table 1. Description of DNHD-DB1

 

In addition, we made human detection algorithm in nighttime image by convolutional neural network (CNN) trained with DNHD-DB1. We also made this trained CNN model open to other researchers.

 

 

(2) DNHD-DB1 & CNN model Request

To gain access to the database and CNN model, download the following DNHD-DB1 & CNN model request form. Please scan the request form and email to Mr. Jong Hyun Kim (zzingae@naver.com).

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

 

Jong Hyun Kim, Hyung Gil Hong, and Kang Ryoung Park, "Convolutional Neural Network-based Human Detection in Nighttime Image Using Visible Light Camera Sensor," accepted for publication in Sensors

 

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< DNHD-DB1 & CNN model Request Form >

 

Please complete the following form to request access to the DNHD-DB1 & 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)

 

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

 

 

 

8. Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)

 

(1) Introduction

This database was collected from 412 persons with different body-view of subjects such as front, back, and side views. The cameras (visible light and thermal cameras) were placed near to each other to create a dual-camera set up, and placed at a height of roughly 6 meters. As the visible light camera, a webcam camera (C600) (https://support.logitech.com/en_us/product/5869) was used, and a Tau2 camera (http://www.flir.com/cores/display/?id=54717) was used for capturing thermal images. The visible camera captures images with a size of 800 × 600 pixels. The thermal camera captures images with a size of 640 × 480 pixels.

For each person, 10 images were respectively captured by visible light and thermal cameras to simulate the variation of body gaits. In total, we collected a database including 8,240 images (4,120 visible light images and 4,120 corresponding thermal images). Among the 412 subjects, there were 254 males and 158 females.

In addition, the following augmented images are included in this database.

 

Table 1. Description of the training and testing databases in our experiments

Database		Males	Females	Total
Training Database	Number of Persons	204 (persons)	127 (persons)	331 (persons)
	Number of Original Images	4080 images (204×20)	2540 images (127×20)	6620 (images)
	Number of Artificial Images	20400 images (204×20×5)	10160 images (127×20×5)	33100 images
Testing Database	Number of Persons	50 (persons)	31 (persons)	81 (persons)
	Number of Original Images	1000 images (50×20)	620 images (31×20)	1620 images
	Number of Artificial Images	5000 images (50×20×5)	3100 images (31×20×5)	8100 images

 

 

(2) DBPerson-Recog-DB1 Request

To gain access to the database, download the following DBPerson-Recog-DB1 request form. Please scan the request form and email to Dr. Dat Tien Nguyen (nguyentiendat@dongguk.edu).

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

 

Dat Tien Nguyen, Hyung Gil Hong, Ki Wan Kim, and Kang Ryoung Park, "Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras," Sensors, Vol. 17, Issue 3(605), pp. 1-29, March 2017

 

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< DBPerson-Recog-DB1 Request Form >

 

Please complete the following form to request access to the DBPerson-Recog-DB1 (All contents must be completed). This database should not be used for commercial use.

 

Name :

 

Contact : (Email)

(Telephone)

 

Organization Name :

 

Organization Address :

 

Purpose :

 

 

Date :

 

               Name (signature)

 

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

 

 

 

9. Dongguk Body-based Gender Database (DBGender-DB2)

 

(1) Introduction

This database was collected from 412 persons with different body-view of subjects such as front, back, and side views. The cameras (visible light and thermal cameras) were placed near to each other to create a dual-camera set up, and placed at a height of roughly 6 meters. As the visible light camera, a webcam camera (C600) (https://support.logitech.com/en_us/product/5869) was used, and a Tau2 camera (http://www.flir.com/cores/display/?id=54717) was used for capturing thermal images. The visible camera captures images with a size of 800 × 600 pixels. The thermal camera captures images with a size of 640 × 480 pixels.

For each person, 10 images were respectively captured by visible light and thermal cameras to simulate the variation of body gaits. In total, we collected a database including 8,240 images (4,120 visible light images and 4,120 corresponding thermal images). Among the 412 subjects, there were 254 males and 158 females.

In addition, the following augmented images are included in this database.

 

Table 1. Description of the training and testing sub-databases and the corresponding augmented databases for our experiments

Database			Males	Females	Total
Augmented database	Learning database	Number of persons	204 (persons)	127 (persons)	331 (persons)
		Number of images	73,440 (204 × 20 × 18 images)	76,200 (127 × 20 × 30 images)	149,640 (images)
	Testing database	Number of persons	50 (persons)	31 (persons)	81 (persons)
		Number of images	18,000 (50 × 20 × 18 images)	18,600 (31 × 20 × 30 images)	36,600 (images)

 

 

(2) DBGender-DB2 Request

To gain access to the database, download the following DBGender-DB2 request form. Please scan the request form and email to Dr. Dat Tien Nguyen (nguyentiendat@dongguk.edu).

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

 

Dat Tien Nguyen, Ki Wan Kim, Hyung Gil Hong, Ja Hyung Koo, Min Cheol Kim and Kang Ryoung Park, "Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction," Sensors, Vol. 17, Issue 3(637), pp. 1-22, March 2017

 

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< DBGender-DB2 Request Form >

 

Please complete the following form to request access to the DBGender-DB2 (All contents must be completed). This database should not be used for commercial use.

 

Name :

 

Contact : (Email)

(Telephone)

 

Organization Name :

 

Organization Address :

 

Purpose :

 

 

Date :

 

               Name (signature)

 

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

 

 

10. Dongguk Activities & Actions Database (DA&A-DB1)

 

(1) Introduction

Our database (shown in Table 1) consists of 12 datasets that have been acquired in different environments and 11 different types of behavior have been included in it. Here, the 11 behaviors include “lying down”, “sitting”, “standing”, “walking”, “running”, “approaching”, “leaving”, “hand waving with two hands”, “hand waving with one hand”, “punching”, and “kicking”. As shown in Table 1, the database has been collected in six different places (“Behind the building including many windows,” … “Car road”) during the night and day, in varying environments (humidity level, wind velocity, temperature, and weather) in four seasons, using different positions of the camera setup of Table 2.

The database includes both visible and thermal images. The database consists of 194,094 images. In our database, the human width varies between 28 and 117 pixels and the height varies in between 50 and 265 pixels.

The cameras (visible light and thermal cameras) were placed near to each other to create a dual-camera set up. As the visible light camera, a webcam camera (C600) (https://support.logitech.com/en_us/product/5869) was used, and a Tau2 camera (http://www.flir.com/cores/display/?id=54717) was used for capturing thermal images. The visible camera captures images with a size of 800 × 600 pixels. The thermal camera captures images with a size of 640 × 480 pixels.

 

Table 1. Description of the all 12 datasets.

Table 2. Details of our camera setup used to collect the 12 datasets (unit: meters).

 

(2) DA&A-DB1 Request

To gain access to the database, download the following DA&A-DB1 request form. Please scan the request form and email to Ganbayar Batchuluun (ganabata87@gmail.com).

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

 

Ganbayar Batchuluun, Jong Hyun Kim, Hyung Gil Hong, Jin Kyu Kang, and Kang Ryoung Park, “Fuzzy System based Human Behavior Recognition by Combining Behavior Prediction and Recognition,” Expert Systems with Applications, Vol. 81, pp. 108-133, September 2017.

 

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< DA&A-DB1 Request Form >

 

Please complete the following form to request access to the DA&A-DB1 (All contents must be completed). This database should not be used for commercial use.

 

Name :

 

Contact : (Email)

(Telephone)

 

Organization Name :

 

Organization Address :

 

Purpose :

 

 

Date :

 

               Name (signature)

 

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

 

 

 

11. Dongguk Body-based Gender Database (DBGender-DB1)

 

(1) Introduction

This database was collected from 412 persons with different body-view of subjects such as front, back, and side views. The cameras (visible light and thermal cameras) were placed near to each other to create a dual-camera set up, and placed at a height of roughly 6 meters. As the visible light camera, a webcam camera (C600) (https://support.logitech.com/en_us/product/5869) was used, and a Tau2 camera (http://www.flir.com/cores/display/?id=54717) was used for capturing thermal images. The visible camera captures images with a size of 800 × 600 pixels. The thermal camera captures images with a size of 640 × 480 pixels.

For each person, 10 images were respectively captured by visible light and thermal cameras to simulate the variation of body gaits. In total, we collected a database including 8,240 images (4,120 visible light images and 4,120 corresponding thermal images). Among the 412 subjects, there were 254 males and 158 females.

 

(2) DBGender-DB1 Request

To gain access to the database, download the following DBGender-DB1 request form. Please scan the request form and email to Dr. Dat Tien Nguyen (nguyentiendat@dongguk.edu).

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

 

Dat Tien Nguyen and Kang Ryoung Park, “Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body,” Sensors, Vol. 16, Issue 7(1134), pp. 1-25, July 2016

 

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< DBGender-DB1 Request Form >

 

Please complete the following form to request access to the DBGender-DB1 (All contents must be completed). This database should not be used for commercial use.

 

Name :

 

Contact : (Email)

(Telephone)

 

Organization Name :

 

Organization Address :

 

Purpose :

 

 

Date :

 

               Name (signature)

 

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

 

 

 

12. Dongguk Face Database (DFace-DB1)

 

(1) Introduction

This database was created by collecting face images from 15 people using conventional web-camera. In order to capture images of users looking at the TV screen naturally, each participant was instructed to watch TV without any restrictions. As a result, we captured a total of 1,350 frames (database I) (15 persons × 2 quantities of participants (1 person or 3 persons) × 3 seating positions (left, middle, and right) × 3 Z distances (1.5, 2, and 2.5 m) × 5 trials (looking naturally)).

In addition, a total of 300 images (database II) (5 persons × 3 Z distances (1.5 m, 2 m, and 2.5 m) × 2 lying directions (left and right) × 10 images) were collected for experiment when each people is lying on his or her side.

For face registration for recognition, additional 75 frames (15 persons × 5 TV gaze points) were obtained at the Z distance of 2 m.

 

(2) DFace-DB1 Request

To gain access to the database, download the following DFace-DB1 request form. Please scan the request form and email to Hyung Gil Hong (hell@dongguk.edu).

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

 

H. G. Hong, W. O. Lee, Y. G. Kim, K. W. Kim, and K. R. Park, “Fuzzy System-Based Face Detection Robust to In-Plane Rotation Based on Symmetrical Characteristics of a Face,” Symmetry-Basel, Vol. 8, Issue 8(75), pp. 1-28, August 2016

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< DFace-DB1 Request Form >

 

Please complete the following form to request access to the DFace-DB1 (All contents must be completed). This database should not be used for commercial use.

 

Name :

 

Contact : (Email)

(Telephone)

 

Organization Name :

 

Organization Address :

 

Purpose :

 

 

Date :

 

               Name (signature)

 

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13. Dongguk Banknote Database (DBanknote-DB1)

 

(1) Introduction

This database was collected for measuring the performance of classifying fitness and unfitness caused by soiling level on banknote. The database includes three datasets such as US dollar (USD), Korean won (KRW), and Indian rupee (INR) datasets. Detail descriptions of each dataset are shown in Tables 1 ~ 4. In all the Tables, direction A shows an image of the banknote in the front side and forward direction, direction B shows an image in the front side and backward direction, C represents an image in the back side and forward direction. Direction D shows the image of the banknote in the back side and backward direction. Based on actual densitometer measurement values about soiling levels, human experts classified the banknotes in the database as fit banknotes (capable of being used) or unfit banknotes (incapable of use).

 

Table 1. Total number of images in USD dataset

 

Table 2. Total number of images in KRW dataset

 

Table 3. Total number of images in INR dataset

 

Table 4. Number of images and classes for USD, KRW and INR datasets

 

 

(2) DBanknote-DB1 Request

To gain access to the database, download the following DBanknote-DB1 request form. Please scan the request form and email to Seung Yong Kwon (sbaru07@dgu.edu).

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

 

Seung Yong Kwon, Tuyen Danh Pham, Kang Ryoung Park, Dae Sik Jeong, and Sungsoo Yoon, "Recognition of Banknote Fitness Based on a Fuzzy System Using Visible Light Reflection and Near-infrared Light Transmission Images," Sensors, Vol. 16, Issue 6(863), pp. 1-18, June 2016

 

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< DBanknote-DB1 Request Form >

 

Please complete the following form to request access to the DBanknote-DB1 (All contents must be completed). This database should not be used for commercial use.

 

Name :

 

Contact : (Email)

(Telephone)

 

Organization Name :

 

Organization Address :

 

Purpose :

 

 

Date :

 

               Name (signature)

 

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