LBP for gait recognition: A symbolic approach based on GEI plus RBL of GEI

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, uniform Local Binary Pattern (LBP) is applied to Gait energy image and to the region bounded by legs (RBL) of Gait Energy Image. Extracted LBP values from GEI and RBL in GEI is represented in terms of interval valued type symbolic features. 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) to study the efficacy of the proposed gait recognition system. The proposed system being robust to handle variations shows significant improvement in recognition rate when compared to the contemporary works on gait recognition.