| Human Identification System Based on Biometrics is a totally brand-new technique different from traditional methods because it adopts the inherent organism's characteristics of human body. More and more people pay great attention to it as it is safer , more reliable and effective. It begins entering every realm of our society and meeting the challenge of new era.In daily life, human's face is frequently used to identify people around and is the most normal pattern in one's vision. The vision information that faces reflect plays an important part in communications and contacts among people. As a result , human's face becomes the easiest acceptable identification method and one of the most potential ones.In this thesis, we first introduced the background and the main methods of the face recognition and then proposed a method of Eigenface based on the wavelet transform for face recognition. Eigenface method for face recognition raised by M.Turk and A.Pentland is still the most popular face recognition algorithm. Although it is simple and effective, the method demands high unitary transform of images of the face imputed. So it is deeply affected by changes of illumination and postures. Therefore in this thesis, we first preprocessed the face images to eliminate the effects of illumination difference of them, and then the wavelet transform was used to obtain the stable low frequency sub-band of the linage in relatively low dimensions. In order to eliminate die correlations between entities of the image vector, the principal component analysis (PCA) was used. Compared with the traditional PCA algorithm, the method proposed here significantly decreases the complexity of the algorithm and the characteristics extracted can have a better representation of the differences among different faces, which results in a high recognition and a high robustness of the algorithm. Experimental results presented in this thesis verified that the proposed algorithm is accurate and effective. |
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