摘要
为提高对光照、表情、姿态等可变因素的鲁棒性,提出一种基于多方向Gabor特征图稀疏表示的人脸识别方法.对人脸图像进行多方向多尺度Gabor变换,然后将同一方向不同尺度的Gabor特征进行融合得到多方向特征图,再对每个方向的融合特征图提取Gist特征并赋予自适应权重,接着将所有方向特征图的自适应加权Gist特征串联构成人脸图像特征向量,最后利用稀疏表示分类方法实现人脸识别.实验结果表明,本文算法在Yale、ORL和ExtendedYaleB人脸数据库上的平均识别率分别达到99.8%、99.7%和100.0%.
In order to improve the robustness to variable factors such as illumination,expression,and pose,a novel face recognition method based on sparse representation with multi-directional Gabor feature maps was proposed in this paper.Firstly,multi-directional and multi-scale Gabor transforms were performed on face image,and the obtained Gabor features with different scales in the same direction were fused to generate multi-directional feature maps.Then,Gist features were extracted and adaptive weights were assigned to them for the fused feature maps in each direction.The adaptive-weighted Gist features of all directional feature maps were cascaded to form feature descriptors of face image.Finally,face recognition was implemented with a sparse representation classification method base on the face feature descriptors.Experimental results show that the average recognition rates of the proposed algorithm on Yale,ORL and Extended Yale B face databases are 99.8%,99.7%and 100.0%respectively.
作者
徐望明
张培
伍世虔
XU Wang-ming;ZHANG Pei;WU Shi-qian(School of Information Science and Engineering,Wuhan University of Science and Technology, Wuhan,Hubei 430081,China;Engineering Research Center of for Metallurgical Automation andDetecting Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan, Hubei 430081,China;School of Machinery and Automation,Wuhan University ofScience and Technology,Wuhan,Hubei 430081,China;Institute of Robotics and IntelligentSystems,Wuhan University of Science and Technology,Wuhan,Hubei 430081,China)
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2019年第7期732-737,共6页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(61775172,61371190)
武汉科技大学研究生创新创业基金资助项目(JCX2016013)
