计算机工程2012，Vol.38Issue(13)：152-155,4.DOI:10.3969/j.issn.1000-3428.2012.13.045
基于PCA特征基压缩传感算法的人脸识别
Face Recognition Based on PCA Feature Set Compressed Sensing Algorithm
摘要
Abstract
This paper presents a face recognition method based on Compressed Sensing(CS) algorithm on PCA feature set for robustness problem caused by occlusion, expression, as well as illumination. It utilizes the Two Directional Two Dimensional PCA((2D)2PCA) transformation to extract image features in both row and column directions and reduce the dimension. A projection matrix is constructed to identify the face features, considering these features to form an over complete dictionary. By solving the l\ norm minimization, the paper finds out the sparsest representation of images based on the dictionary to obtain a set of optimal sparse coefficient, which is used to recover the train images, computes the residuals between test and train images for face recognition. Experimental results show that this method not only has a high recognition rate in a lower dimension, but also reduces the computational complexity. Thus it can effectively improve the face recognition robustness on occlusion and expression, as well as illumination.关键词
人脸识别/压缩传感/稀疏表示/最小化l1范数/鲁棒性Key words
face recognition/ Compressed Sensing(CS)/ sparse representation/ minimization l1 norm/ robustness分类
信息技术与安全科学引用本文复制引用
张尤赛,赵艳萍,朱志宇..基于PCA特征基压缩传感算法的人脸识别[J].计算机工程,2012,38(13):152-155,4.基金项目
国家自然科学基金资助项目(61075028) （61075028）
