Face Recognition With Small Sample Based On FKPCA+Double Subspace And Information Attribute KNN

Face recognition technology has been attached great importance to the researchers for its Scientific significance and practical value in the past few years,and become the hotspot of current pattern recognition and artificial intelligence.Face recognition normally be regarded as have three processes that are face detection, features extraction and pattern classification.Face recognition often meet these problems,the dimension of sample too high,the classes of pattern too mach and each person could only provide a small amount training sample.In this paper,by systematically analyzing of relevant algorithms,we present some novel algorithms for face recognition with small sample based on FKPCA+Double Subspace and Information Attribute KNN classifier,from three aspects of the speed of extracting features,information completeness and face recognition.In addition,a prototype system of face recognition is designed and implemented.The highlights and main contributions of the dissertation include:(1) A novel method based on FKPCA+Double Subspace for features extraction is presented.Firstly,FKPCA is used to map input space information to High-dimensional space to reduce the dimension of original samples in High-dimensional space;secondly,get the regular information using Fisher Criterion in Rang Space and gain the Irregular information employing between-class scatter Criterion in Null Space.This method can extract more complete optimal discriminant features,and be also of great help to the feature extraction problem in small sample case.(2) The Information Attribute K-nearest Neighbor classifier is studied.On the base of thorough study of traditional K-nearest neighbor classifier,against the problem of Euclidean Distance of KNN can not exptress sample semblable degree well,the Strategy of using information attribute as Weights is adopted to improve Euclidean Distance;then the impoved Euclidean Distance is used as K-nearest neighbor classifier measure.The Information Attribute K-nearest Neighbor classifier can effectively make up the fault that Euclidean Distance render only the truly distance of two points in m dimension space.(3) An algorithm on Face Recognition with Small Sample is given. Firstly,FKPCA is used to implement linearly Dimension Reduction;secongly, extract Regular information and Irregular information;thridly,Information Attribute Euclidean Distance is used to carry out of calculating the sample semblable degree with Regular information and Irregular information alone.Then,the calculations have Worked out are fused.At last,K-nearest neighbor classifier the task of classification with the fused result.(4) Based on the idea of object-oriented,we design and development a prototype system of face recognition with small Sample,which is divided into four modules which are image preprocess,FKPCA process,Double Subspace feature extraction and Information Attribute KNN face recognition.And makes the system recognize people according to face image with only a little training samples.System has managed to maintain a higher correct recognition rate.