Research On Human Gait Recognition

Biometrics makes use of the physiological or behavioral charactristics of people to authenticate their identities. With the growing need for a full range of visual surveillance and monitoring system in security-sensitive envirenments such as airports, bus stops and banks, human identification at a distance has recently gained increasing interest from computer vision researchers. To operate successfully, the established biometrics such as face, fingerprints and iris usually require proximal sensing or physical contact. However they are hardly applicable at a distance. Fortunately, gait is still visible and can be easily perceived unobtrusively. So, gait is a very attractive modality from the visual surveillance perspective.Gait recognition is a relatively new research area. It aims to seek distinguishable variations between the same actions of walking from different people for the purpose of automatic identity verification. Focusing on this topic, this dissertation mainly includes the following issues:1. Silhouette extraction is an important procedure in gait recognition, which includes two steps:background modeling and motion extraction. In this dissertation, Kernel Gaussian Model is used to model the gait background, and an effective kernel width estimation method is proposed. After gait images are mapped into probability images by the statistical background model, foreground pixels are extracted from these probability images by the EM algrithem and motion slice analysis. Experiment result shows that the proposed method achieves obvious improvement than traditional methods for the complicated scenes. And at the meantime, it can satisfy the requirement for real-time background modeling.2. Gait features are extracted by the model-based method of skeletonization under the situation that silhouette quality is relatively good. First, an improved distance transform method is proposed to extract the raw skeleton; then, the main branches of human body are extracted by the skeleton erosion and restoration technique; lastly, the periodic motion charactaristcs of human main skeleton are treated as the final gait feature to identify people. Experiment result shows that the skeleton feature of gait can achieve highly satisfying performance in indoor environment. Especially for multipul view settings, the recognition rate can be significantly increased by fixing the view angle of skeletons.3. A novel gait recognition method based on spatiotemporal feature extraction is proposed under the situation of bad silhouette quality. In the first subspace learning, the periodic dynamic feature of gait is extracted by Principal Component Analysis and sequence data is represented in the Periodicity Feature Vector form; in the second subspace learning, Principal Component Analysis integrated with Linear Discriminant Analysis are applied to the Periodicity Feature Vector representation of gait and sequence data is compressed into Gait Feature Vector. Gait Feature Vector contains both the shape and the dynamic information of human gait, which shows strong discriminative ability. Experimental result shows that the proposed method achieves highly competitive performance and it takes less storeage and computational cost.