Comparative Study of Gait Gender Identification using Gait Energy Image (GEI) and Gait Information Image (GII)

Abstract

Identifying gender from the pedestrian video is one crucial key to study demographics in such areas. With current video surveillance technology, identifying gender from a distance is possible. This research proposed the utilization of computer vision to identify gender based on their walking gait. The data feature used to determine gender based on their walking gait divided into five parts, namely the head, chest, back, waist & buttocks, and legs. Two different methods are used to perform the real-time gender gait recognition process, i.e., Gait Energy Image (GEI) and Gait Information Image (GII), while the Support Vector Machine (SVM) method used as the data classifier. The experimental results show that the process of identifying gender based on walking with GEI method is 55% accuracy and GII method is 60% accuracy. From these results, it can conclude that the method GII with SVM classifier has the best accuracy in the process of gender classification