Research On The Method Of Human Identification Recognition Based On Gait

Gait is defined as the coordinated, cyclic combination of movements that result in human locomotion, and it is a behavior characteristic on human walking posture which belongs to one of human biological features. Recently, the technology for human identification recognition based on gait is attractive since it has the characteristics for example, no subject contact, easy collection at a distance and hard to disguise etc al. Existing biometric system are never perfect for all required performances and environments, therefore, gait faces some inevitable difficulties for data changes in different viewpoints, data changes for wearing clothing and package, data change for a long time, etc al. The paper aims to the human object abstraction, elliptical model recognition, optical flow effect on gait and dimensional reduction and the main contributions in detail as follows:Aiming to rapidity for moving objection and accuracy contour, the algorithm of threshold function on second order difference and improved GVF is raised. The initial contour is established by threshold function, which is built by experience and displacement equation. Subsequently, the reinitial contour for improved GVF according to rectangle range is obtained by linear rules, and is used to extract moving object. The method compared to other GVF algorithm is not only simple, but also has little iterations and more rapidity. Aiming to the more accurate contour extraction in single view-point, a new method based on human template matching and improved GVF snake is proposed in the paper. It can segment object accuratly, eliminate the effect of shadow more effectively and make solid foundation for gait recognition.Aiming to discontinuous moving object contour in low resolution images, a robust recognition algorithm based on improving ellipse model of orthogonal contacting points is raised, which need not closed object contour. A robust key fame, not dynatic time programming, makes detecting key fames easily and accurately. According to elliptic parameters in the key frames, correct classification rate(CCR) for gait recogntion is higher, and it reduces calculation complexity, and fitting result is better than algebraic one.In order to benefit from static and dynamic information in walking posture and improve correct classification rate, an optical flow method based on pixels in neighbor area is proposed. The results display that the correct recognition rate(CCR) for compontes on syntheses and moving direction is higher to100percents almostly, and contrary moving direction along human walking has lowest CCR. On total, the raised optical flow algorithm in neighbor pixels is better than simple contour pixels.Aiming to much information in gait sequence, manifold algorithm according with congnitive law is used to map the intrinsic dimension data in high dimension into lower dimension, in which is recognized. Therefore, locally linear embedding(LLE) manifild to reduce dimensionality is researched in detail, and an algorithm based on weighted distance testing and LLE train is raised. The expermental results show that the CCR of raised algorithm is higher than LLE and weighted LLE method, parameters for raised method are chosen easily, it runs rapidly than weighted LLE and is alike LLE in time.