Investigating the separability of features from different views for gait based gender classification

In this paper, we investigate the efficiency of different view angles when classifying gender with gait biometrics for the first time. A gait database is built for this purpose in which walking videos are recorded at seven different views for each subject. Then, we employ a robust gait representation method to extract gait features. The class separability of these features from different view angles are analyzed and compared. A set of experiments are designed to evaluate the performance of gait based gender classification along with the changes of view angle. The experimental results show that 0deg and 180deg are the worst view angles in this two-category case and the 90deg view dose not perform the best, unlike it takes the best performance in gait recognition.