The gait and face image processing

Dealt with the deficiency of single creature feature classification, classification methods of gait and facial side integrating at the feature layers are brought forth to improve the identify classification rate with long distance. This paper respectively has feature extraction and dimension reduction process for analyzing gait energy diagram and side facial image by multivariate discriminant analysis for Matrix component analysis of dichroic image to get original feature matrix to make vectorication, fusing feature eigenvector, and then feature fusion for fusing feature eigenvector by multiple discriminant analysis techniques to obtain the fusing eiqenvector of gait and facial image, at final nearest neighbour methods identifies identity. Testing for the above-mentioned data by CASIA Dataset B gait database. The results show that it improves the accurate classification rate to test the effectiveness of this method providing a new way for multi-biometric features classification. Introduction Kale, etc. uses mono vidicon to make fusing classification for invariant optic angle gait and facial classification decision layer. Shakhnarovich, etc uses visible shell generation method for four vidicons to shoot a serious of mono visual diagram for rebuilding 3D model to follow and recognize the visible regulated object. Zhou, etc designs a classification system by using single vidicon to get side human body video to extract the gait outline and side face to identify the fusing of gait and facial image. Zhou and Bhanu, etc uses mono vidicon to identify the long distance gait and side facial image on matching layer and feature layer. Li Yi, etc makes classification based on Fourier transform and singular score decomposition methods. Liu Huanxi uses classification by single mode creature feature of sustance learning algorithms. Preprocessing for Gait and Facial Image For gait diagram sequence, make background modeling to get background image. Foreground aimed human image obtained by background subtraction uses connected analysis to eliminate small area to get rather complete aimed human image. Binaryzation for aimed human image operates by mathematical morphological methods to fill in the cavity and get rid of noise fringed on gait image to test the outline sequence with cycle detection to compound gait energy diagram. In addition, in preprocessing for gait and facial image, the complete foreground aimed human image extracts head section and graying to test side face. A periodical sequence two score gait outline image generates GEI like shown as Figure 1. The head image position of face is like shown as Figure 2. Fig.1. GEI 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) © 2016. The authors Published by Atlantis Press 1321 Fig.2. Schematic diagram side facial detection Fusing Classification for Gait and Face Image. Video image sequence gets gait energy diagram and side human diagram by gait and human image preprocessing to fuse and identify the gait energy image and side facial image on feature layer. Combining 2DIMPCA and multiple discriminant analysis brings a kind of classification of gait and facial feature fusing. Feature extraction There are g g s r  testing sample gait energy diagram of g n like g n X X X , , , 2 1  and f f s r  testing sample side face image of f n like f n A A A , , , 2 1  . The IMPCA operation respectively are for testing sample gait energy diagram and side facial image on the horizontal direction to find the best projection matrix g U and f U . The population variance matrix of gait energy diagram and side facial diagram are respectively: