Semi-supervised Gait Recognition Based on Self-Training

Abstract

Traditional gait recognition researches focus on supervised learning methods that use only a limited number of labeled sequences to train, which will definitely restrict the recognition ability of the gait recognition system. Meanwhile, training with more typical gait sequences can improve the generalization ability of gait recognition system and eventually achieve better recognition accuracy. However, it is difficult, expensive, time consuming and boring to capture enough gait sequences comparing with capturing other biometric traits such as fingerprint, face and iris during the enrolment stage. To address the problem, a semi-supervised gait recognition algorithm based on self-training is proposed to optimize the performance of gait recognition system with both a few labeled sequences and a large amount of unlabeled sequences. Nearest Neighbor (NN) classifier and K-Nearest Neighbor (KNN) classifier are carried out to recognize the different subjects. Experimental results show that the proposed algorithm has an encouraging recognition performance even with only one labeled sequence each class.