融合局部形变模型的鲁棒性人脸识别  

Robust Face Recognition by Fusion Local Deformable Model

作　　者：廖海斌[1] 陈庆虎[1] 王宏勇[2] 

机构地区：[1]武汉大学电子信息学院,武汉市珞喻路129号430079 [2]河南工业大学信息科学与工程学院

出　　处：《武汉大学学报（信息科学版）》2011年第7期877-881,共5页Geomatics and Information Science of Wuhan University

基　　金：中央高校基本科研业务费专项基金资助项目(20102120103000004);河南省科技攻关计划资助项目(092102210398);郑州市重大科技攻关计划资助项目(072SGZS38042)

摘　　要：将测试图像看成是人脸库的线性组合,并用形变模型表示,优化匹配求解组合系数,根据系数的稀疏性进行分类识别。为了进一步提高算法的鲁棒性,采用了分片加权的策略。在公用的人脸数据库上进行广泛的实验,结果表明,平均识别率达到97%以上,在遮挡30%时其识别率仍达到95%以上。本方法对人脸识别问题非常有效,且可以显著提高对伪装、遮挡变化的鲁棒性和稳定性。At present,practical face recognition faces the problem of the variation of illumination,expression,pose and the occlusion,disguise.The problem of robust recognizing human faces with varying expression and illumination,as well as occlusion and disguise will be researched in this paper.We cast the test face image as a linear combination of face database and used deformable model representation,Match optimization deformable model for solving combinatorial coefficient,according to the sparse nature of coefficients for classification.Conducting extensive experiments on publicly available databases show that： the average recognition rate is higher than 97%,and the recognition rate is still more than 95% with test image of block 30%.This method is very effective for face recognition and can significantly improve the robustness and stability of disguise or occlusion.

关 键 词：人脸识别 遮挡和伪装 形变模型 

分 类 号：P237.3[天文地球—摄影测量与遥感]