INDIVIDUAL GAIT RECOGNITION USING PARTICLE SWARM TEMPLATE SEGMENTATION - M. Hema, K. Babulu, N. Balaji (International Journal of Advanced Science and Technology, 2020)
Human identification by GAIT recognition now a day’s dealing with great challenges in biometrictechnology. In this paper, the Particle Swarm Template Segmentation method is proposed to identifythe person using their GAIT. The PSTS method employs the Particle Swarm Optimization algorithm,which is a relatively recent metaheuristic population-based swarm intelligence optimization technique.A comparative analysis has been done on existing and proposed methods to evaluate the performanceof Particle Swarm Template Segmentation (PSTS)over another metaheuristic evolutionary basedgenetic Template segmentation (GTS),using Gait Energy Image and Gradient Gait Energy Imagetemplates of gait sequences from CASIA – B (Chinese Academy of Sciences) dataset. Further PrincipalComponent Analysis and Linear Discriminate Analysis are used for accuracy by reducingdimensionality in our data in GAIT recognition. The results depict that the for individual gaitrecognition, the Gradient Gait Energy Image (GGEI) appears to display the best outcome whensegmented with the Particle Swarm Optimization algorithm compare to Genetic Template Segmentation(GTS)