Multimodal Feature Learning for Gait Biometric Based Human Identity Recognition

摘要

In this paper we propose a novel multimodal feature learning technique based on deep learning for gait biometric based human-identification scheme from surveillance videos. Experimental evaluation of proposed learning features based on novel deep learning and standard (PCA/LDA) features in combination with classifier techniques (NN/MLP/SVM/SMO) on different datasets from two gait databases (the publicly available CASIA multiview multispectral database, and the UCMG multiview database), show a significant improvement in recognition accuracies with proposed fused deep learning features.