Gait Recognition Method Research On Features Fusion And SVM

Recognition by gait is a new field for the biometric recognition technology. Its aim is to recognize people or detect physiological, pathological and mental characters by their walk style. Gait recognition, as one of the attractive research area of biomedical information detection, attracts more and more attention. Gait recognition analysis of human usually includes gait motion segment, feature extract, and recognition. This interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual user interface. This paper mainly discusses gait image sequence detection, feature extraction and classification recognition of the visual analysis. This paper provides a comprehensive survey of recent developments of vision-based human motion analysis, and it keeps up with the latest research.At first, gait sequences are preprocessed. By analyzing and comparing kinds of motion detection methods, and considering the simple background of gait sequences, background subtraction is used in gait detection. Gait cycle is analyzed, then width and height of body analysis is performed to computer it.Next, in order to solve the problem that most gait recognition methods based on single feature can not get satisfied recognition results, according to the idea of feature fusion, a gait recognition method using fusing of lower-limb angles and body silhouette at score lever is proposed. Each feature is assigned to weights, which can make them combine in suitable proportion.Finally, the SVM classifier is used to complete object recognition. Support Vector Machine which aim at small sample statistical estimates and projections learning is the best theory and identification methods. Then, the hybrid kernel is as kernel function.This method captures the movement characteristics of gait and gait shape features, the simulation results on the database show that the proposed algorithm has an encouraging recognition performance with relatively lower computational cost. It is an effective gait feature extraction and recognition method.