Gait Analysis of Gender and Age Using a Large-Scale Multi-view Gait Database

This paper describes video-based gait feature analysis for gender and age classification using a large-scale multi-view gait database. First, we constructed a large-scale multi-view gait database in terms of the number of subjects (168 people), the diversity of gender and age (88 males and 80 females between 4 and 75 years old), and the number of observed views (25 views) using a multi-view synchronous gait capturing system. Next, classification experiments with four classes, namely children, adult males, adult females, and the elderly were conducted to clarify view impact on classification performance. Finally, we analyzed the uniqueness of the gait features for each class for several typical views to acquire insight into gait differences among genders and age classes from a computer-vision point of view. In addition to insights consistent with previous works, we also obtained novel insights into view-dependent gait feature differences among gender and age classes as a result of the analysis.