Gait identification using component based gait energy image analysis

In the modern era of computer vision technology, gait biometric trait increases the proliferation of human identification in video surveillance situation. This paper intends to discuss the robustness of gait identification irrespective of small fluctuation in subject's walking pattern. The Gait Energy Image (GEI) is computed on silhouette gait sequences obtained from OU-ISIR standard gait database. The advantage of working with GEI is to preserve the shape and motion information into a single averaged gait image with fewer dimensions. The three independent components such as head node, body torso and leg region are separated from subject's GEI in accordance to body segment ratio. The local biometric feature has been computed from the shape centroid to the boundary points of each segment. The normality testing of feature for each region of GEI body frame ascertains the discriminative power of each segment. The similarity measurement between gallery and probe gait energy image has been computed by cosine distance, correlation distance and Jaccard distance. The performance efficiency of different distance based metrics is measured by several error metrics.