Deep Features Based Multiview Gait Recognition

Abstract

Pattern recognition is one of the computing intensive application. As information technology has evolved to such an extend, it is easier to identify various patterns in images or videos. As an example consider fingerprint identification, which is image pattern identification application widely used in smartphones to authenticate a person. Similarly Gait recognition, which is a video pattern identification application, is used for surveillance purpose. A lot of research is going on gait recognition. In our purposed deep Convolutional Neural Network (CNN) architecture, Gait Energy image is used as the input and simple several layers are used in matrix analytics. The softmax classifier is used to measure the similarity between Gallery gaits and Probe gaits. This paper uses CASIA Dataset B for performance evaluation of Gait recognition using different angle view variation, normal walking, wearing clothing and carrying bags conditions. The experimental results show that the proposed deep CNN architecture’s accuracy is better than Bag-of-Words (BoW), Histogram of Image Gradient (HOG) and 3D-Histogram of Merged Orientations (3D-HOMO)