Research And Comparison Of Face Recognition Algorithm

Face recognition technology has a wide range of applications in various fields of identification.Face recognition technology includes image acquisition from high-definition cameras,image preprocessing after image acquisition,processing of data using the popular face recognition algorithm for image features Collection,and finally through the feature matching to identify the database in the face and other aspects.At present,the main research direction is how to adopt the appropriate pretreatment method and optimize the extraction of the characteristics of the algorithm.In this paper,we introduce the techniques of gray image transformation and sharp-ening to preprocess the original image,which can make the image clearer and the fea-tures of the image are more prominent.We introduce two kinds of gray and linear transformation.The classical feature extraction method is the PCA(principal component analysis)method proposed by M.Turk and A.Pentland in the paper "Eigenfaces for Recogni-tion".This method can extract some of the secondary information by extracting the characteristic part of the image,so that the image feature is highlighted,and the o-riginal high-dimensional image becomes a simple low-dimensional data image,which not only retains the most information,Improve the efficiency of identification.After 2000,Yang Jian,Chen Fu Bing,Han Xiaocui and others based on the traditional P-CA method,in turn proposed an optimized 2DPCA method,module 2DPCA method,based on the middle value of the improved 2DPCA method,have achieved good results.In this paper,the main feature extraction method is the traditional PCA method,the whole process of PCA algorithm is deduced,and the whole process of face recog-nition is completed by PCA method.At the same time,two other methods,such as 2DPCA,were compared and analyzed,and their advantages and disadvantages were analyzed.The contribution of the article is to provide a complete face recognition solu-tion,the innovation lies in the traditional classical PCA method of the feature space for weighted processing.Because the first few maximum vector values of the eigenvector are affected by the illumination,the effect of illumination on the image can be reduced by the weighted processing.The numerical results show that the face recognition rate of the PCA method in some coefficients is higher than that of the PCA method and 2DPCA method works better,which can better improve the face recognition rate.Of course,you can also use the same idea for the 2DPCA method for weighting the same,but the results need to follow the experimental study.