Research On Face Feature Extraction And Classification Algorithm

Face recognition as a non-contact nature, easy collection, identification technology theory, field of use is constantly evolving and expanding. To complete the identification of face images must be extracted stable facial features, and the most commonly used features of shape, intensity distribution, the frequency characteristics of three. The shape and relatively susceptible to gray light, angle, and expression; frequency characteristics are usually in close contact with the wavelet transform, the frequency characteristics of a low-level features are relatively stable, but can not be directly used for matching and recognition; people in the process of matching and recognition presented in a number of ways to solve a particular problem, but the recognition rate and the reaction time is usually not ideal. Therefore, this paper proposes a face recognition algorithm based on digital image processing and support vector machine to study and attempt to resolve the issue.The basic idea of the algorithm proposed in this paper is:first experimental material ORL face database to light face image processing; then go light on the use of the image histogram equalization technique for image enhancement processing, enhanced image clarity; thus using two-dimensional wavelet transform image decomposition, reducing the dimension of the image processing; then using2D-PCA algorithms for image feature extraction; Finally, support vector machine method for image matching and recognition. The algorithm especially in the two-dimensional wavelet transform decomposition of the image, the relevant characteristics of the source with the highest degree of similarity; introducing2D-PCA algorithm significantly reduces the computational complexity; support vector machine for matching and recognition, better to avoid some of the features can not be classified in the low dimensional space and other issues.After comparative experiments show that the proposed algorithm compared with other algorithms, the recognition rate for face recognition and response time higher efficiency with higher recognition accuracy and better able to achieve the recognition requirements.