The face database is aiming at real-world applications of face recognition. In order to simulate real-world settings, we capture faces with different poses, facial expressions, accessories and illuminations.
Finger vein recognition is a recently developed research hotspot. We include in
SDUMLA-HMT a finger vein database which, to the best of our knowledge, is the
first open finger vein database.
The device used to capture finger vein images is designed by Joint Lab for Intelligent Computing and Intelligent Systems of Wuhan University. In the capturing process, each subject was asked to provide images of his/her index finger, middle finger and ring finger of both hands, and the collection for each of the 6 fingers is repeated for 6 times to obtain 6 finger vein images. Therefore, our finger vein database is composed of 3,816 images. Every image is stored in "bmp" format with 320×240 pixels in size,and thus, the finger vein database takes up around 0.85G Bytes in total.
Three variations, including view angle, accessory and motion type, are considered in the gait data acquisition process. For each subject, we first captured 6 background videos using the 6 cameras before his/her walking. Then the subject was asked to walk naturally along the walking direction for 6 times. After that, the subject was asked to carry a bag and walked twice again. The bag could be a knapsack, a satchel, or a handbag chosen according to the subject's preference. Furthermore, we also recorded twice of the subject's running videos.As a result, there are totally 66 videos
recorded for the subject.The gait database is composed of 6×11×106=6,996 videos in total and each video
records about 2 to 3 gait cycles. All the videos are stored in “avi” format encoded
with XviD codec. The frame size is 320×240 pixels, and the frame rate is 25 frames
per second. The total size of the gait database is about 1.6G Bytes.
We collected the iris data with an intelligent iris capture device developed by University of Science and Technology of China under near infrared illumination. To avoid reflection, the subjects were asked to take off their glasses and to keep the distance between the eye and the device within 6cm to 32cm. Every subject provided 10 iris images, i.e., 5 images for each of the eyes. Therefore,the iris database is composed of 2×5×106=1,060 images. Every iris image is saved
in 256 gray-level “bmp” format with 768 × 576 pixels in size. The total size of our
iris database is about 0.5G Bytes.
The multi-sensor fingerprint database includes fingerprint images captured from thumb finger,
index finger and middle finger of both hands. In order to explore the sensor interoperability,
we captured each of the 6 fingers with 5 different type of sensors, i.e.,
AES2501 swipe fingerprint scanner developed by Authentec Inc, FPR620 optical
fingerprint scanner and FT-2BU Capacitive fingerprint scanner both developed by
Zhongzheng Inc, URU4000 optical fingerprint scanner developed by Zhongkong Inc
and ZY202-B optical fingerprint scanner developed by Changchun Institute of Optics,
Fine Mechanics and Physics, China Academy of Sciences. It is to be noted that 8
impressions were captured for each of the 6 fingers using each of the 5 sensors.The multi-sensor fingerprint database contains 6×5×8×106=25,440 fingerprint images in total.
Every fingerprint image is saved in 256 gray-level “bmp” format but the size varies
according to the capturing sensors.The
total size of the multi-sensor fingerprint database is about 2.2G Bytes.
PS:The fingerprint images are named in the format of “fingeridx_n.bmp”, where fingeridx = (1, 2, …, 6) is the finger index (i.e., 1 for left thumb, 2 for left index, 3 for left middle, 4 for right thumb, 5 for right index, and 6 for right middle), and n is the repeated impression index ranging from 1 to 8.