Human Gait Recognition by using Two Stream Neural Network along with Spatial and Temporal Features

Human Gait Recognition (HGR) is referred to as a biometric tactic that is broadly used for the recognition of an individual by using the pattern of walking. There are some key factors such as angle variation, clothing variation, foot shadows, and carrying conditions that affect the human gait. In this work, a new approach is proposed for the HGR that contains five major steps. In the first step, the video data is converted into image frames. In the second step, RGB to GRAY conversion is carried out. After that, a two-stream network is designed by using a 55-layer CNN model called CNN-55 trained on CIFAR-100. The CNN-55 is designed from scratch and trained on the CIFAR-100 dataset by selecting hyperparameters. This pre-trained CNN-55 is used to build a two-stream network. In Stream-1 the optical flow frames are obtained by Horn and Schunk algorithm. These frames are fed into a CNN-55 to extract temporal features. In Stream-2 the GRAY frames are fed to the CNN-55 model for extraction of spatial features. After that, both vectors are serially fused. In the fourth step, the fused feature vector is fed into the Genetic Algorithm for optimization. Finally, the feature vector is fed into the One-Versus-All SVM classifier for recognition. The system is tested on all CASIA-B angles such as 000, 180, 360, 540, 720, 900, 1080, 1260, 1440, 1620, and 1800 which provides accuracy of 97.10%, 96.80%, 94.60%, 98.0%, 98.30%, 96.80%, 97.60, 96.90%, 99.60%, 96.80%, and 97.60%, respectively. The proposed method produces better outcomes compared to recent techniques.