Gait Recognition Based On Pelma Pressure Feature

Gait recognition is an advanced recognition method based on biological feature and also one of the important researches attracting many researchers and research institutions both at home or international area. Gait recognition combines the computer vision, pattern recognition and image sequence technologies with each other in the field of computer and signal processing so that it has great application prospect. Gait recognition has its special recognition feature as well as it can overcome the shortcomings from traditional identity recognition, and its potential usage is to become the auxiliary of the current recognition technology, in addition, can be divided into a new and remote concealment type of identification technology. This paper focuses on the press value of static gait matrix and deals with them like analysis, feature extraction and recognition course. Mainly, this paper pays attention on the two types of pressure features so as to achieve the goal of recognition. Detail research contents and experiment results were as follow:1. Introducing the history and development of gait recognition and discussing the current status, specifically, the theory basement and independent advantage of the gait recognition of computer vision and mechanics analysis.2introducing the basic situation of the database of gait recognition at home or inter nation. Particularly this paper talks about the instruments of data acquisition and the information of database in the paper. Besides, it also explains the course of rotation course of the static gait image and the selection of classifier.3Taking advantage of the feature extraction based on pressure value of static gait matrix and acquiring some feature totally based pressure and pressure distribution, such as Euclidean distance between pressure points, the coordinate distribution of pressure points and the relationship between partial pressure points. All these features are based on the static pressure value and distribution.4Taking advantages of some other features that are not based the information of pressure and also some features combines with pressure. For instance, the geometrical features of static gait image and the feature from the combination between geometrical feature and pressure feature. In fact, some of these features can achieve a better recognition rate and some are not.