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In this paper, we consider the problem of gender recognition based on face and multi-view gait cues in the same walking sequence. The gait cues are derived from multiple simultaneous camera views. Meanwhile, the face cues are captured by a camera at front view. According to this setup, we build a database including 32 male subjects and 28 female subjects. Then, for face, we normalize the frame images decomposed from videos and introduce PCA to reduce image dimension. For gait, we extract silhouettes from videos and employ an improved spatio-temporal representation on the silhouettes to obtain gait features. SVM is then used to classify gender with face features and gait features from each view respectively. We employ three fusion approaches involving voting rule, weighted voting rule and Bayes combination rule at the decision level. The effectiveness of various approaches is evaluated on our database. The experimental results of integrating face and multi-view gait show an obvious improvement on the accuracy of gender recognition.

Gender Recognition Based on Fusion of Face and Multi-view Gait