| Summary: | 碩士 === 國立中央大學 === 資訊工程研究所 === 97 === Most face recognition methods assume either constant lighting condition or natural facial expressions and hence can not deal with both kinds of variations simultaneously. The constraint has to be alleviated in a reliable and practical face recognition system.
In order to resolve the aforementioned problem, we present a component-based face recognition system which can deal with both illumination and facial expression variations with the using of only one training sample image per class. In our work, retinex algorithm is firstly adopted to decrease the influence of illumination variation. Then, active appearance model (AAM) is employed to extract facial features to establish the proposed face recognition system. Next, support vector machine (SVM) is utilized to distinguish the variations of facial expressions by using the mouth features. To equip with the
capability of insensitivity to expressions, the proposed system decreases the weights of these features which are affected by facial expressions. Finally, the recognition part combines the global feature and local features to generate the recognition result.
Experimental results demonstrate that the proposed component-based face recognition system can indeed improve the performance when the images are under different illumination and facial expression variations.
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