Abstract
Gait is a unique perceptible biometric feature at larger distances, and the gait representation approach plays a key role in a video sensor-based gait recognition system. Class Energy Image is one of the most important gait representation methods based on appearance, which has received lots of attentions. In this paper, we reviewed the expressions and meanings of various Class Energy Image approaches, and analyzed the information in the Class Energy Images. Furthermore, the effectiveness and robustness of these approaches were compared on the benchmark gait databases. We outlined the research challenges and provided promising future directions for the field. To the best of our knowledge, this is the first review that focuses on Class Energy Image. It can provide a useful reference in the literature of video sensor-based gait representation approach.
Highlights
Over the past ten years, gait recognition, which utilizes the manner of walking to identify individuals, has obtained extensive interest in the communities of biometric recognition and video surveillance [1,2,3,4,5,6,7,8,9]
This paper has presented a comprehensive review of the video sensor-based gait representation methods, especially spatio-temporal motion summary approaches, namely Class Energy Image approaches
We have reviewed and analyzed various video-based Class Energy Image approaches, which have the following properties: (1) They contain rich motion information such as motion frequency, temporal and spatial changes of the human body; (2) They compress the information of a sequence to a template, which reduces the size of the gait database; (3) They are suitable for real time systems because Class Energy Image has a high computational efficiency; (4) They are insensitive to the quality of silhouettes and robust to silhouette errors or image noise
Summary
Over the past ten years, gait recognition, which utilizes the manner of walking to identify individuals, has obtained extensive interest in the communities of biometric recognition and video surveillance [1,2,3,4,5,6,7,8,9]. Each frame of a walking sequence is fitted to the model of the human body and the parameters, such as motion trajectories [46], joint angles [5], hip position [47], limb lengths [48], body part ellipses [49] and physical distances [50], gathered from moving bodies are measured on the model as gait features for recognition One such approach represents a gait silhouette as seven regions of ellipses. The advantages of the video sensor-based Class Energy Image approach can be summarized as follows: (a) It is well suitable for real time systems because it is easy to extract the feature and computational complexity is low [21,60];.
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