| dc.description.abstract | The relevant research of face recognition has have to be strengthen and overcome rate of accuracy of recognition. The previous treatment steps and content of input image obviously affect accuracy of recognition. The so-called content of image means that the environment and the changes of lighting on face, and this factor affects the rate of recognition very deeply. Most previous research focus on taking whole face image for recognition. This thesis focuses on dividing the face image into different areas, then takes these different areas to recognize. The face image is often full of skin areas, which have less help to recognition but damage. Furthermore, the color of skin areas is easily influenced by the lighting condition or changes of the environment. In order to prevent the above drawbacks which may decrease the accuracy of face recognition, the paper propose a method which fetches different areas of the face as features and these areas have little skin color areas .
The algorithm of face recognition consists of LDA + PCA and before recognition we must first do wavelet transform on the image. The reason doing wavelet transform is to keep the changeless parts of image, in other words it can remove some unnecessary parts of image. In addition, the size of image will dwindle too, so the time to recognize will be reduced. After finishing of recognition, due to several areas to recognize, several results of recognition will be produced. So generally speaking in combining these result of recognition, people usually adopts the vote method, but in addition to voting method, we also adopted weighting approach in this thesis. The weight value was based on the results of features which were clustered. We adopt weighting method when the vote method is unable to decide. The experimental results showed that our proposed approach which is based on certain blocks in the face is better than other methods which using the entire face image in accuracy rate. | en_US |