TY - JOUR
T1 - Multimodal camera-based gender recognition using human-body image with two-step reconstruction network
AU - Baek, Na Rae
AU - Cho, Se Woon
AU - Koo, Ja Hyung
AU - Truong, Noi Quang
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2020 Inderscience Enterprises Ltd.. All rights reserved.
PY - 2019
Y1 - 2019
N2 - With the recent development of intelligent surveillance systems, the importance of research study on gender recognition of people at a distance is also on the rise. The existing gender recognition technologies studies have used high-resolution facial images captured from the front at a short distance, which showed high performance. However, intelligent surveillance systems in actual real environments have difficulty in detecting the faces of people because they use images captured from a distance. Moreover, in the case of back-view images, gender recognition based on the facial image is impossible because the face cannot be detected. Thus, gender recognition using the full-body human-body images of people is being studied but its performance is low owing to problems such as low resolution, motion blur, and optical blur. Furthermore, the performance of gender recognition using only visible-light cameras is limited owing to illumination variations, shadow, and the type of clothes and accessories. To solve these problems, remote body-shapebased recognition was performed by sequentially using two convolutional neural networks which improved the resolution of visible-light images. In addition, the degradation of recognition performance owing to various factors (e.g., illumination, shadow, and the type of clothes and accessories) was prevented by combining a visible-light camera with an infrared camera, and the scalability was enhanced using various heterogeneous cameras. The higher performance of the proposed method compared with that of other methods was verified through a comparative experiment using the open database of Sun Yat-sen University multiple modality Re-ID (SYSU-MM01) and the Dongguk body-based gender database (DBGender-DB2) that has been built by us.
AB - With the recent development of intelligent surveillance systems, the importance of research study on gender recognition of people at a distance is also on the rise. The existing gender recognition technologies studies have used high-resolution facial images captured from the front at a short distance, which showed high performance. However, intelligent surveillance systems in actual real environments have difficulty in detecting the faces of people because they use images captured from a distance. Moreover, in the case of back-view images, gender recognition based on the facial image is impossible because the face cannot be detected. Thus, gender recognition using the full-body human-body images of people is being studied but its performance is low owing to problems such as low resolution, motion blur, and optical blur. Furthermore, the performance of gender recognition using only visible-light cameras is limited owing to illumination variations, shadow, and the type of clothes and accessories. To solve these problems, remote body-shapebased recognition was performed by sequentially using two convolutional neural networks which improved the resolution of visible-light images. In addition, the degradation of recognition performance owing to various factors (e.g., illumination, shadow, and the type of clothes and accessories) was prevented by combining a visible-light camera with an infrared camera, and the scalability was enhanced using various heterogeneous cameras. The higher performance of the proposed method compared with that of other methods was verified through a comparative experiment using the open database of Sun Yat-sen University multiple modality Re-ID (SYSU-MM01) and the Dongguk body-based gender database (DBGender-DB2) that has been built by us.
KW - Convolutional neural network
KW - Gender recognition
KW - Human-body image
KW - Image reconstruction
KW - Visible-light and infrared cameras
UR - http://www.scopus.com/inward/record.url?scp=85089947213&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2932146
DO - 10.1109/ACCESS.2019.2932146
M3 - Article
AN - SCOPUS:85089947213
SN - 2169-3536
VL - 7
SP - 104025
EP - 104044
JO - IEEE Access
JF - IEEE Access
M1 - 2932146
ER -