| Summary: | 碩士 === 國立交通大學 === 電機學院電機與控制學程 === 99 === This thesis adopts skin-color model to find the candidate face region, then Gabor wavelets transformation is adopted to extract the entire face features. Afterward, neural network is trained to determine whether the candidate region is a human face or not. Finally, this thesis adopts active appearance model and steerable filter to normalize all faces for face recognition. Then this thesis implements sparse coding algorithm with 5 training faces to increase the face recognition rate up to 80% for photographs, and for frontal face of AR database also increases by 98%. Furthermore, this thesis proposes using histogram method to reduce 60% of sparse coding needed which also reduces the amount of system computational cost, and then the features are still representative. As a whole, this system is suitable for digital media classification of family photograph albums or digital photograph frames.
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