Motion-Based Gait Recognition for Recognizing People in Traditional Gulf Clothing

Abstract

Gait recognition is gaining popularity as it can recognize people in a non-intrusive and a non-contact manner. However, gait recognition is known for its susceptibility to clothing conditions. In this paper, we propose a solution specific to clothing conditions in the Gulf region where Abaya and Kandura are considered traditional clothing. The paper proposes a solution capable of training users based on traditional clothing and recognizing them in Western style clothing and vice-a-versa. The solution uses depth imaging, optical flow, accumulated motion and Discrete Cosine Transformation (DCT). Motion is calculated from consecutive images where the magnitudes and phases of motion vectors are accumulated into separate matrices. DCT and zonal coding is then applied to these matrices to form one concise feature vector that represents a walk. Experimental results, with 38 participants, showed that the proposed method is suitable for gait recognizing with such clothing constraints. The average classification accuracy is 88%. In comparison to an existing method, it is shown that the proposed method results in much more accurate recognition results yet at a higher computational cost.