Automatic Extraction of Human Gait Feature for Recognition

Human gait is a new biometric resource in visual surveillance system. It can recognize individual as the way they walk. In the walking process, the human body shows regular periodic variation, such as upper and lower limbs, knee point, thigh point, height, etc. which reflects the individual’s unique movement pattern. However from a computational perspective, it is quite difficult to extract some feature points (knee, thigh, leg, and hip) because of occlusion of clothes, carrying bags. Height is one of the important features from the several gait features which is not influenced by the camera performance, distance and clothing style of the subject. This paper proposes DLT method of predicting height variation signal from the gait cycle of each subject. Height estimation has done using calibrated camera images. The variation of height signal is further analyzed using various transform: DHT, DFT, and DCT. Euclidian distance and MSE are computed on feature vectors to recognize individual.