Research On 2D And Bimodal Hybrid For Face Recognition With A Sigle Training Sample

The face recognition with a single training sample means that each person only save one face image as training set to recognize the identity of face image whose attitude and light can be change. The problem of single training sample has important significance and brings large challenge, so it has become an important research direction of face recognition. This paper analysed the research status of face recognition with a single training sample, and indicated that sample augment and multi-feature hybrid are efficient way to solve the problem of single training sample. This paper mainly focuses on theory and algorithm to research the face recognition with a single training sample of 2D and bimodal hybrid, especially research the 2D face image feature extraction and 2D face and 3D nose shape bimodal information hybrid.The main research work is as follows:1. This paper proposed a synthesis method of 3D face model based on single frontal face image through studying the sample augment of face recognition with a single training sample. This method use synthetical 3D face model to creat virtual face images whose attitude, light and expression can change to expand training sample set, so the problem of single training sample is translated into face recognition with multi-training sample. The 3D model recovering by 2D image is a basic problem in computer vision filed. Conventional method is impractical because it needs many face images and image sequences, or 3D image pair, frontal and side image pair in limited condition. The method proposed in this paper is suitable for practical application and has a broad application prospects because it only needs one frontal face image which reduces the demand for use condition. The synthetical 3D face model method can satisfy the demand for face recognition, expression animation and human-computer exchange. The experiment based on virtual image proves that the sample expanding method can solve the problem of illumination, gestureand and expression in face recognition.with a single training sample efficiently. Meanwhile, it extracts 3D nose shape from the generated 3D face model and using it to recognize people, the experiment proves the feasibility of 3D nose shape as a novel biometric identification mode.2. From the view of increasing single mode, which is called 2D face recognition performance, this paper improves the conventional 2D face feature extraction method, combines kernel trick and manifold learning algorithm, proposes Non-locality Preserving P(?) ction(NLPP) algorithm and 2DLPP algorithm aiming at face image vector and matrix and (?) (?)pares two feature extraction algorithms based on face image vector and matrix respective (?) from the recognition rate, training and recognition time. The experiment proves that the methods proposed can not only extract the non-linear information from the image but also hold the neighbour-keeping characteristic. Thus, better recognition results can be gotten.3. This paper has deeply researched the subspace method based on image matrix, proposed three feature extraction methods which are modular bi-direction weighting 2D major component analysis, modular multi-projection bi-direction 2D linearity discriminant analysis and modular 2D discriminant locality preservation major component analysis. The experimental results indicated that the proposed algorithms have better feature extraction performance than previous algorithms. It means that it is benefit to pattern recognition by changing the conventional algorithm.4. From the view of information hybrid, this paper proposed a new way that 2D face recognition and 3D nose shape recognition should be fused in decision level to solve the problem that the face recognition with a single training sample has low recognition rate and credibility. Two hybrid methods namely the parallel structure based on improved weighting voting method and serial structure based on double-layer screening model have proposed focusing on 1:1 Authentication and 1:N identification model. The experiment shows that face recognition framework based on 2D face and 3D nose shape information hybrid is a suitable method to solve single training sample problem and it has many advantages comparing with single identity recognition method. Firstly,2D information can be easy to catch, and can be treated by many related mature techniques; simultaneity,3D information including 3D shape properties of the object which can be supplement with 2D information and preferably solve the problem of accouterment and expression in face recognition.