Symbolic Representation and Recognition of GAIT : An Approach Based on LBP of Split GAIT Energy Images

Abstract

Gait is one of the biometric techniques used to identify an individual from a distance by his/her walking style. Gait can be recognized by studying the static and dynamic part variations of individual body contour during walk. In this paper, an interval value based representation and recognition of gait using local binary pattern (LBP) of split gait energy images is proposed. The gait energy image (GEI) of a subject is split into four equal regions. LBP technique is applied to each region to extract features and the extracted features are well organized. The proposed representation technique is capable of capturing variations in gait due to change in cloth, carrying a bag and different instances of normal walking conditions more effectively. Experiments are conducted on the standard and considerably large database (CASIA database B) and newly created University of Mysore (UOM) gait dataset to study the efficacy of the proposed gait recognition system. The proposed system being robust to handle variations has shown significant improvement in recognition rate.