Gender classification based on fusion of multi-view gait sequences

In this paper, we present a new method for gender classification based on fusion of multi-view gait sequences. For each silhouette of gait sequences, we first use a simple method to divide the silhouette into 7 (for 90 degree, i.e. fronto-parallel view) or 5 (for 0 and 180 degree, i.e. front view and back view) parts, and then fit ellipses to each of the regions. Next, the features are extracted from each sequence by computing the ellipse parameters. For each view angle, every subject's features are normalized and combined as a feature vector. The combination of feature vector contains enough information to perform well on gender recognition. Sum rule and SVM are applied to fuse the similarity measures from 0°, 90°, and 180°. We carried our experiments on CASIA Gait Database, one of the largest gait databases as we know, and achieved the classification accuracy of 89.5%.