| Summary: | 碩士 === 國立臺灣科技大學 === 機械工程系 === 100 === Face recognition across illumination variation involves illumination normalization, feature extraction and classification. This research compares a few state-of-the-art illumination normalization methods, and selects the most potential one. We also investigate the impacts made by different facial regions on the recognition performance. Many believe that the facial region considered for face recognition is better bounded within the facial contour to minimize the degradation due to background and hair. However, we have found that the inclusion of the boundary of the forehead, contours of the cheeks, and the contour of the chin can effectively improve the performance. Minimum average correlation energy filter (MACE) combined with kernel class-dependence feature analysis (KCFA) is proven an effective solution, and therefore is adopted in this study with some minor modification. Following the protocol FGRC 2.0, the recognition rate can be improved from 72.91% to 84.83% using the recommended illumination normalization, and further improved to 88.17% with the recommended facial region.
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