Face Recognition Using Weber Local Gradient Descriptor based Sparse Representation

• Main Authors: Sheng-BinKe, 柯盛彬
• Other Authors: Yen-Tai Lai
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/x2n3aw

碩士 === 國立成功大學 === 電機工程學系 === 104 === Face recognition plays an important role nowadays. It is critical in a wide range of applications such as mug-shot database matching, credit card verification, security system, and scene surveillance. A large amount of works have been done in face recognition. Most of them deal with uncontrolled variations such as changes in illumination, pose, expression and occlusion individually. 　　In this thesis, variable illumination and occlusion are mainly discussed. We propose an approach combining sparse representation based classification (SRC) and varied weber local gradient descriptor (VWLGD) to deal with them. It is effective in variable illumination by using VWLGD. Then the processed images can be classified with SRC to achieve the goals. Experimental results show that this method achieves high recognition rates, and is quite effective in variable illumination and occlusion.