Person Identification Using Gait Biometric with Challenging Clothing and Speed Cofactors

Abstract

Biometrics are the entire class of technologies and techniques utilized to identify the individual by using their physiological or behavioral attributes. The way of human walking, the most emergent and unique biometric signature allows automatic gait-based person identification. Gait identification task becomes more difficult due to the change of appearance by different cofactors (e.g., walking speed, shoe, surface, carrying, view, clothing and etc.). The main goal of this thesis is to develop novel methods to address the two most frequently happening covariate factors clothing and carrying condition and walking speed changes for gait recognition. These cofactors may affect some parts of gait while other parts remain unchanged and can be used for recognition. An algorithm is proposed to define which parts are more effective and which parts are less effective for cofactors like walking speed, clothing, carrying objects etc. It is found that, for clothing and carrying conditions the upper part of the body is more affected whereas for walking speed changes the lower body part is more affected. During the process of finding the effective body parts, the whole body is divided into small segments where each segment is a single row in this work. Based on positive and negative effect of each segment in terms of recognition rate, we define the whole gait into five unequal parts for clothing and carrying conditions. Usually, the dynamic areas (e.g., legs, arms swing) are comparatively less affected than static areas (e.g., head, torso) for different cofactors in appearance-based gait representation. To give more emphasis on dynamic areas and less on static areas, frequency-domain gait entropy termed as EnDFT representation is proposed and used as gait features. Experiments are conducted on two comprehensive benchmarking databases: The OU-ISIR Gait Database, the Treadmill dataset B with huge clothing variations and CASIA Gait Database, Dataset B with clothing and carrying conditions. The proposed method achieved the recognition rate 72.78% for OU-ISIR and 77.69% for CASIA gait database at rank-1 and presented better results in comparison with other existing gait recognition approaches.