Multiview gait-based gender classification through pose-based voting

Abstract

The incorporation of soft biometrics can significantly increase the performance of hard biometric systems. Gender is found to be the most popular soft biometric that can be derived from human gait. The state of the art produces accuracies up to 98% but are constrained by the necessity of requiring a complete gait cycle to function properly. We propose to remove this requirement using pose-based voting (PBV) – a method which treats every frame as a labeled instance. Linear discriminant analysis (LDA) is used in conjunction with the Bayes’ rule for classification as an alternative to the popular support vector machine (SVM). The robust design of this technique facilitates the system to cope with partially occluded gait cycles with minimal loss in classification accuracy. Furthermore, when multiple cycles are taken to account, the error becomes negligibly small. We also investigate the applicability of elliptic Fourier descriptors and and depth gait histograms for the gait-based gender classification problem. The efficiency of our approach is evaluated using all view angles of the CASIA-B gait database and the TUM-GAID database under prescribed test conditions.