Research On Face Recognition Based On Eigen-Face Technology

Face recognition is a major direction of biology recognition technology. It is one of the most extensively used technologies. Compared with other biometrics, face recognition has features such as initiative, non-invasive and user-friendliness.Face recognition is the specific use of pattern recognition in the image field. It has broad application prospect, such as identification of identity documents, automated access control system, criminal detection, video surveillance, home security and other fields.Face recognition system includes face image preprocessing, feature extraction and face recognition three parts.Face image preprocessing is an important step for face recognition process. When the input image from the actual scene into digital image information, there are often the defect such as existence of noise and illumination changes due to equipment conditions, such as the degree of illumination bright-dark, as well as the merits of performance. So image preprocessing is a necessary work before the extraction of characteristics. Color image gray processing, median filter, histogram equalization,face location and geometric scaling are used to eliminate these effects and improve the recognition rate.The feature extraction based on the combining of wavelet transform and discrete cosine transform is used in the paper. Image is decomposed using wavelet transform and its low-frequency part reserves the majority of information and energy. At the same time, the relatively larger feature vector modulus is generated in the sensitive location of the image after the wavelet transform. Discrete cosine transform is an orthogonal transformation. A variety of orthogonal transformation can reduce the relevance of random vector in a certain extent and the energy will be concentrated on a small number of transform coefficients when the signal was transformed by most of the orthogonal transformation. This can be proved in the mathematics. Those are useful for face recognition when these advantages were used in face image. In this algorithm, first of all, face image after preprocessed is decomposed by wavelet transform twice, and then the low-frequency components are transformed by discrete cosine transform to extract the feature and are compressed. 100 discrete cosine transform coefficients are extracted to be the last feature values. Finally, euclidean distance and nearest neighbor classifier are used to recognizing target. Based on above theory, the face recognition technology coule be applied in many fields. For example: the access control system. First, we can preprocess the given face image. Second, feature value is extracted. At last, comparing with face image and the model in the face database to determine whether it belongs to the face database. If so, access control system will be opened and this can realize the purpose of automatic recognition. The results of experiment indicate the new method of the technology of human face recognition is available.