Abstract
In the thesis, we focus on how to reconstruct the illumination normalized face image which can be used to improve face recognition result. Here, we employ a framework to process large-scale and small-scale features image independently. In this frame work, first, we decompose the face image into a large-scale feature image and a small-scale feature image. Second, once a face image is decomposed into a large-scale feature image and a small-scale feature image, normalization is then mainly performed on the large-scale feature image. Then, a smooth operator is applied on the small-scale feature image. Finally, we combine the large- and small- scale feature images to generate an illumination normalized face image. We test our method by a face recognition algorithm using Yale B & Extend Yale B database which contains 38 subjects under 64 different illumination conditions. The experimental result shows that image processed by our method not only has better visual quality, but also improves the performance of face recognition.