Face Recognition Based On Kernel Discriminant Analysis

Human face detection and recognition play critical roles in many applications such as video surveillance and face image database management.In our project,we have studied,were working on both face recognition and detection techniques and developed algorithms for them.In face recognition the algorithm used is a PCA(principal component analysis)MPCA(Multilinear Principal Component Analysis)and LDA(Linear Discriminant Analysis)in which we recognize an unknown test image by comparing it with the known training images stored in the database as well as give information regarding the person recognized.These techniques work effectively under robust conditions like complex background,different face positions.These algorithms give different rates of accuracy under abnormal conditions as experimentally observed..This paper also be focuses on the problem of face recognition face image preprocessing.We consider the use of kernel principal component analysis and kernel Fisher linear discriminant for learning low dimensional representations for face recognition.In the our new approach,the kernel trick is used firstly to project the original samples into an implicit space called feature space by nonlinear kernel mapping then two equivalent models based on generalized Fisher criterion have established by the theory of Reproducing Kernel in the feature space and the optimal discriminant vectors are solved finally by using the technique of orthogonal complementary space.The development and mainly methods of face recognition technique is approved in the research.Then the problem of preprocessing,feature extraction and classification is discussed.In the face recognition Digital image processing as computer-based technology carries out automatic processing manipulation and interpretation of such visual information,and it plays an increasingly important role in many aspects of our daily life as well as in a wide variety of disciplines and fields in science and technology with applications such as television,photography robotics,remote sensing,medical diagnosis and industrial inspection.The development and mainly methods of face recognition technique is introduced in this paper.Then the problem of preprocessing,feature extraction and classification are discussed the aim of face image preprocessing is to regularize the face image which is captured by image collecting devices to normalized an image,it includes two steps mainly geometry normalization and grey value normalization The kernel trick is used firstly to project the input data into an implicit space called feature space by nonlinear kernel mapping face feature extraction and classification made some theoretical and experimental research design the developers and mainly methods of face recognition technique are first introduced in this paper.Then the problem of preprocessing,feature extraction and classification is discussed the aim of face image preprocessing is to regularize the face image which is captured by image collection devices to normalise image it includes two steps mainly geometry normalization and gray value normalization.A central issue to a successful approach in face recognition I show to extract discriminate feature form the facial images linear and nonlinear kernel projection principle component analysis method based on Principal Component Analysis(PCA)and MPCA(Multi linear Principal Component Analysis)and LDA(Linear Discriminant Analysis)in which we recognize an known test image by comparing it with the known training images stored in the database as well as give information regarding the person recognized.These techniques work well under robust conditions like complex background,different face positions.These algorithms give different rates accuracy under different conditions as experimentally observed.