Research On Gait Recognition Based On Fusion

The world we live in is so diverse and complicated that we need higher and higher security guarantee in every situation. Its advantageous characteristics such as safety, efficiency and unique etc. make biometrics identification technology (biometrics) valued by more and more people gradually. Gait recognition, which is to identify one's identity according to the different ways one walks, is a rising biometrics. And because of its incomparable crypticity, noninvasive and low demand of image resolution ratio which other biometrics doesn't have, it has an extensive prospect of application in the vision monitoring area.The process of gait recognition is mainly made up of three parts, that is, pretreatment of gait image , feature extraction and discrimination. Pretreatment of gait image is to input detected moving targets of the image sequence via background modeling, future check and the morphology reprocessing. It is the foundation of following extracting features and classifying objective subjects, which is significant. Of all the work, extracting features is the most important, and it is the decisive factor in an effect of discrimination. And because of this, it is a key point this paper deals with. In view of side vision, this paper comes up with two ways of extracting features.One is based on Radon transformation. That is, Radon transformation used in line detection is extended to the foundation of feature space. In line with characteristics of human motion, we can combine space-time invariant physical structure parameters of moving human with Time-varying motion parameters, making up a periodic eigen vector template. Then we can use Principal Component Analysis(PCA) to reduce the feature space dimension to extract main vctor of characteristics. The other way is to use standard deviation of each object's gait energy image(GEI) which can reflect the dynamic information as dynamic weight mask(DWM). After correcting, we can use Hadamard product with GEI to get enhanced gait energy image(EGEI) whose dynamic information is strengthen. Not only does it continue to have the gait information of contour, frequency, phase etc. but also it solves occlusion issue to a degree. Then, we can use Two Directional Two Dimensional Princdipal Component Analysis((2D)2PCA) to calculate prineipal components of training and testing samples, and we get eigenvector matrix which contributes most to identification.There are limitations in single gait description to gait characteristics. The text keeps to the key point that different gait characteristics can provide complementary information when identifying, adopts methods based on score and fuses together information of two kinds of character at decision layer.The experiment adopts CASIA which contains 124 objects and Dataset B of 3 walking conditions. Using above ways of algorithm, we test and assess its distinctions. The results show that the algorithm referred to in the paper which extracts feature by enhanced gait energy image, has a perfect robustness to clothes varying and carried materials. And it's better to merge together two sorts of characteristic information by reasonable rules to improve its distincions.