Research And Realization In Face Detection And Recognition Algorithms

Biometrics is a kind of science and technology using individual physiological or behavioral characteristics to verify identity. It provides a highly reliable and robust approach to the identity recognition. Automatic face detection and recognition is one of the most attention branches of biometrics and it is also the one of the most active and challenging tasks for image processing, pattern recognition and computer vision. It is widely applied in commercial and law area, such as mug shots retrieval, real-time video surveillance in security system and cryptography in bank and so on. Face recognition has direct, friendly characteristics and it is no psychological obstacle for users. This dissertation mainly studies the approaches to frontal face detection and recognition. The main research works and contributions are as the following.(1)The biometrics technology and its development, application, and signification is summarized. The research content, approach and development are emphasized. The research status is introduced. The technology of the face detection and recognition are summarized.(2) A color space based on non-linear transformation is proposed. The face skin segmentation is finished in this non-linear color space. The face skin classification based on algorithms of adaptive threshold of region growing is realized. The skin regions and non-skin regions are separated with the fuzzy segmentation of adaptive threshold. The eyes and mouth are located with the multi-scale morphological algorithms. The face is located by the texture and geometrical features of face, then it is tested whether the candidate region is the face or not.(3)Symmetrical Principal Component Analysis (SPCA) and Kernel Principal Component Analysis (KPCA) are proposed based on the Classical Principal Component Analysis (CPCA). The face image is decomposed to odd and even images by introducing the mirror example to extract the odd and even symmetrical Karhunen-Loeve features. The features are select based on the different proportion of feature component in the face image and the different sensitivities in visual angle, rotation and illumination. The odd and even orthonormal reconstructure in the algorithm is proposed theoretically and higher correct. Recognition rate is achieved for the face with SPCA. Its main idea is that CPCA is first employed to preprocess the original training images before the nonlinear mapping and KPCA is used to extract features. The experimental results on ORL face databases indicate that the proposed method is more effective.(4)The paper proposes a classification method based on wavelet transform and features correlation. Its main idea is that the wavelet transform is first employed to preprocess the...