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Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment | |
Zhuang, Liansheng1; Chan, Tsung-Han2; Yang, Allen Y.3; Sastry, S. Shankar3; Ma, Yi4 | |
2015-09 | |
发表期刊 | INTERNATIONAL JOURNAL OF COMPUTER VISION |
ISSN | 0920-5691 |
卷号 | 114期号:2-3页码:272-287 |
发表状态 | 已发表 |
DOI | 10.1007/s11263-014-0749-x |
摘要 | Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required gallery images to one sample per class. To compensate for the missing illumination information traditionally provided by multiple gallery images, a sparse illumination learning and transfer (SILT) technique is introduced. The illumination in SILT is learned by fitting illumination examples of auxiliary face images from one or more additional subjects with a sparsely-used illumination dictionary. By enforcing a sparse representation of the query image in the illumination dictionary, the SILT can effectively recover and transfer the illumination and pose information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the state of the art in the single-sample regime and with less restrictions. In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class. Furthermore, the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization. |
关键词 | Single-sample face recognition Illumination dictionary learning Sparse illumination transfer Face alignment Robust face recognition |
收录类别 | SCI ; EI |
语种 | 英语 |
资助项目 | Science Foundation for Outstanding Young Talent of Anhui Province[BJ2101020001] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000360071900010 |
出版者 | SPRINGER |
EI入藏号 | 20143600027095 |
EI主题词 | Algorithms ; Alignment ; Crime ; Silt |
EI分类号 | Soils and Soil Mechanics:483.1 ; Mechanical Devices:601.1 ; Telecommunication; Radar, Radio and Television:716 ; Computer Software, Data Handling and Applications:723 ; Mathematics:921 ; Social Sciences:971 |
WOS关键词 | ALGORITHM ; MODELS |
原始文献类型 | Article |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/2149 |
专题 | 信息科学与技术学院 信息科学与技术学院_PI研究组_马毅组 |
通讯作者 | Yang, Allen Y. |
作者单位 | 1.Univ Sci & Technol China, CAS Key Lab Electromagnet Space Informat, Hefei, Peoples R China 2.Adv Digital Sci Ctr, Singapore, Singapore 3.Univ Calif Berkeley, Dept EECS, Berkeley, CA 94720 USA 4.ShanghaiTech Univ, Shanghai, Peoples R China |
推荐引用方式 GB/T 7714 | Zhuang, Liansheng,Chan, Tsung-Han,Yang, Allen Y.,et al. Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2015,114(2-3):272-287. |
APA | Zhuang, Liansheng,Chan, Tsung-Han,Yang, Allen Y.,Sastry, S. Shankar,&Ma, Yi.(2015).Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment.INTERNATIONAL JOURNAL OF COMPUTER VISION,114(2-3),272-287. |
MLA | Zhuang, Liansheng,et al."Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment".INTERNATIONAL JOURNAL OF COMPUTER VISION 114.2-3(2015):272-287. |
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