Mutual Component Analysis for Heterogeneous Face Recognition
文献类型：期刊论文
| 作者 | Li, Zhifeng1; Gong, Dihong1; Li, Qiang2; Tao, Dacheng2 ; Li, Xuelong3
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| 刊名 | acm transactions on intelligent systems and technology
 |
| 出版日期 | 2016-04-01 |
| 卷号 | 7期号:3 |
| 关键词 | Algorithms Performance Face recognition heterogeneous face recognition mutual component analysis (MCA) |
| ISSN号 | 2157-6904 |
| 产权排序 | 3 |
| 英文摘要 | heterogeneous face recognition, also known as cross-modality face recognition or intermodality face recognition, refers to matching two face images from alternative image modalities. since face images from different image modalities of the same person are associated with the same face object, there should be mutual components that reflect those intrinsic face characteristics that are invariant to the image modalities. motivated by this rationality, we propose a novel approach called mutual component analysis (mca) to infer the mutual components for robust heterogeneous face recognition. in the mca approach, a generative model is first proposed to model the process of generating face images in different modalities, and then an expectation maximization (em) algorithm is designed to iteratively learn the model parameters. the learned generative model is able to infer the mutual components (which we call the hidden factor, where hidden means the factor is unreachable and invisible, and can only be inferred from observations) that are associated with the person's identity, thus enabling fast and effective matching for cross-modality face recognition. to enhance recognition performance, we propose an mca-based multiclassifier framework using multiple local features. experimental results show that our new approach significantly outperforms the state-of-the-art results on two typical application scenarios: sketch-to-photo and infrared-to-visible face recognition. |
| WOS标题词 | science & technology ; technology |
| 学科主题 | 计算机应用其他学科（含图像处理） |
| 类目[WOS] | computer science, artificial intelligence ; computer science, information systems |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | discriminant-analysis ; spectral regression ; sketch recognition ; classification ; performance ; framework |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000373911200003 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/27880]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing 100864, Peoples R China 2.Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, 81 Broadway, Ultimo, NSW 2007, Australia 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Zhifeng,Gong, Dihong,Li, Qiang,et al. Mutual Component Analysis for Heterogeneous Face Recognition[J]. acm transactions on intelligent systems and technology,2016,7(3). |
| APA | Li, Zhifeng,Gong, Dihong,Li, Qiang,Tao, Dacheng,&Li, Xuelong.(2016).Mutual Component Analysis for Heterogeneous Face Recognition.acm transactions on intelligent systems and technology,7(3). |
| MLA | Li, Zhifeng,et al."Mutual Component Analysis for Heterogeneous Face Recognition".acm transactions on intelligent systems and technology 7.3(2016). |
入库方式： OAI收割
来源：西安光学精密机械研究所
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