A comprehensive study on gait biometrics using a joint CNN-based method. (September 2019)

• Record Type: Journal Article
• Authors: Zhang, Yuqi; Huang, Yongzhen; Wang, Liang; Yu, Shiqi
• Is Part Of: Pattern recognition. Volume 93(2019:Sep.)
• Page Start: 228
• Page End: 236
• Publication Date: 2019-09
• Subjects: Gait recognition -- Soft biometrics -- Joint learning -- Network visualization; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.04.023
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: This paper gives a comprehensive study on gait biometrics via a joint CNN-based method. Gait is a kind of behavioral biometric feature with unique advantages, e.g., long-distance, cross-view and non-cooperative perception and analysis. In this paper, the definition of gait analysis includes gait recognition and gait-based soft biometrics such as gender and age prediction. We propose to investigate these two problems in a joint CNN-based framework which has been seldom reported in the recent literature. The proposed method is efficient in terms of training time, testing time and storage. We achieve the state-of-the-art performance on several gait recognition and soft biometrics benchmarks. Also, we discuss which part of the human body is important and informative for a specific task by network visualization.