Multi-view Gait Recognition System Using Spatio-Temporal Features and Deep Learning

Systems based on physiological biometrics are ubiquitous but requires subject cooperation or high resolution to capture. Gait recognition is a great avenue for identification and authentication due to uniqueness of individual stride in an un-intrusive manner. Machine vision systems have been designed to capture the uniqueness of stride of a specific person but factors such as change in speed of stride, view point, clothes and carrying accessories make gait recognition challenging and open to innovation. Our proposed approach attempts to tackle these problems by capturing the spatio-temporal features of a gait sequence by training a 3D convolutional deep neural network (3D CNN). The proposed 3D CNN architecture tackles gait identification by employing holistic approach in the form of gait energy images (GEI) which is a condensed representation capturing the shape and motion characteristics of the the human gait. The network was evaluated on two of the largest publicly available datasets with substantial gender and age diversity; OULP and CASIA-B. Optimization strategies were explored to tune the hyper-parmeters and improve the performance of the 3D CNN network. The optimized 3D CNN and the GEI were effectively able to capture the unique characteristics of the gait cycle of an individual irrespective of the challenging covariates. State of the art results achieved on the multi-views and multiple carrying conditions of the subjects belonging to CASIA-B dataset demonstrating the efficacy of our proposed algorithm.