Speed, Cloth and Pose Invariant Gait Recognition-Based Person Identification

Abstract

Gait is very important to identify person from distance. It requires very less interaction with human participants. Gait is considered the popularly known visual identification technique. The major challenges associated with gait-based person identification are high variability, gait occlusion, pose and speed variance and uniform gait cycle detection, etc. In this research work, the CASIA-A, B and C data set is explored for the view, cloth and speed invariant person identification to address the challenged associated with gait-based person identification. In this work, the very important technique of computer vision for object identification is being explored. It included feature extraction techniques, namely gait energy image(GEI) for cloth invariance, histogram of gradients(HOG) for multiview invariance and Zernike moment with random transform for crossview invariance. To classify data, SVM, ANN and XGBoost-based machine learning algorithms are used on the CASIA gait data set and achieved 99, 96 and 67% identification accuracy, respectively, for three different scenarios of invariance, i.e. speed, cloth and pose.