Dense-View GEIs Set: View Space Covering for Gait Recognition based on Dense-View GAN

Liao, Rijun1; An, Weizhi2; Yu, Shiqi3; Li, Zhu1; Huang, Yongzhen4,5

10.1109/IJCB48548.2020.9304910

2020

IEEE/IAPR International Joint Conference on Biometrics (IJCB)

2474-9680

978-1-7281-9187-4

IEEE/IAPR INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB 2020)

1-9

SEP 28-OCT 01, 2020

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Gait recognition has proven to be effective for long-distance human recognition. But view variance of gait features would change human appearance greatly and reduce its performance. Most existing gait datasets usually collect data with a dozen different angles, or even more few. Limited view angles would prevent learning better view invariant feature. It can further improve robustness of gait recognition if we collect data with various angles at 1 degrees interval. But it is time consuming and labor consuming to collect this kind of dataset. In this paper, we, therefore, introduce a Dense-View GEIs Set (DV-GEIs) to deal with the challenge of limited view angles. This set can cover the whole view space, view angle from 0 degrees to 180 degrees with 1 degrees interval. In addition, Dense-View GAN (DV-GAN) is proposed to synthesize this dense view set. DV-GAN consists of Generator, Discriminator and Monitor, where Monitor is designed to preserve human identification and view information. The proposed method is evaluated on the CASIA-B and OU-ISIR dataset. The experimental results show that DV-GEIs synthesized by DV-GAN is an effective way to learn better view invariant feature. We believe the idea of dense view generated samples will further improve the development of gait recognition.

Gallium nitride Monitoring Face recognition Legged locomotion Generators Training Robustness

其他

英语

EI ; CPCI-S

NSF I/UCRC[1747751]

Computer Science ; Engineering ; Mathematical & Computational Biology

Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Mathematical & Computational Biology

WOS:000723870900056

20210409827436

Biometrics ; Data acquisition

Bioengineering and Biology:461 ; Biomechanics, Bionics and Biomimetics:461.3 ; Data Processing and Image Processing:723.2

Web of Science

1.Univ Missouri Kansas City, Dept Comp Sci & Elect Engn, Kansas City, MO 64110 USA
2.Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA
3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
5.Watrix Technol Ltd Co Ltd, Beijing, Peoples R China

Liao, Rijun,An, Weizhi,Yu, Shiqi,et al. Dense-View GEIs Set: View Space Covering for Gait Recognition based on Dense-View GAN[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:1-9.