| Biologically Inspired Tensor Features | |
| Mu, Yang1; Tao, Dacheng1; Li, Xuelong2; Murtagh, Fionn3 | |
| 作者部门 | 光学影像分析与学习中心 |
| 2009-12-01 | |
| 发表期刊 | COGNITIVE COMPUTATION
 |
| ISSN | 1866-9956 |
| 卷号 | 1期号:4页码:327-341 |
| 摘要 | According to the research results reported in the past decades, it is well acknowledged that face recognition is not a trivial task. With the development of electronic devices, we are gradually revealing the secret of object recognition in the primate's visual cortex. Therefore, it is time to reconsider face recognition by using biologically inspired features. In this paper, we represent face images by utilizing the C1 units, which correspond to complex cells in the visual cortex, and pool over S1 units by using a maximum operation to reserve only the maximum response of each local area of S1 units. The new representation is termed C1 Face. Because C1 Face is naturally a third-order tensor (or a three dimensional array), we propose three-way discriminative locality alignment (TWDLA), an extension of the discriminative locality alignment, which is a top-level discriminate manifold learning-based subspace learning algorithm. TWDLA has the following advantages: (1) it takes third-order tensors as input directly so the structure information can be well preserved; (2) it models the local geometry over every modality of the input tensors so the spatial relations of input tensors within a class can be preserved; (3) it maximizes the margin between a tensor and tensors from other classes over each modality so it performs well for recognition tasks and (4) it has no under sampling problem. Extensive experiments on YALE and FERET datasets show (1) the proposed C1Face representation can better represent face images than raw pixels and (2) TWDLA can duly preserve both the local geometry and the discriminative information over every modality for recognition. |
| 文章类型 | Article |
| 关键词 | Biologically Inspired Features C1 Units Manifold Learning Discriminative Locality Alignment Face Recognition |
| 学科领域 | 电子、电信技术 |
| WOS标题词 | Science & Technology ; Technology ; Life Sciences & Biomedicine |
| DOI | 10.1007/s12559-009-9028-5 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | OBJECT RECOGNITION ; FACE-RECOGNITION ; LAPLACIANFACES ; CLASSIFICATION ; EIGENFACES ; COMPONENTS ; MECHANISMS ; MODELS ; CORTEX |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Neurosciences & Neurology |
| WOS类目 | Computer Science, Artificial Intelligence ; Neurosciences |
| WOS记录号 | WOS:000207987300004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/8040 |
| 专题 | 瞬态光学研究室 |
| 作者单位 | 1.Nanyang Technol Univ, Singapore 639798, Singapore 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China 3.Univ London, Dept Comp Sci, Egham TW20 0EX, Surrey, England |
| 推荐引用方式 GB/T 7714 | Mu, Yang,Tao, Dacheng,Li, Xuelong,et al. Biologically Inspired Tensor Features[J]. COGNITIVE COMPUTATION,2009,1(4):327-341. |
| APA | Mu, Yang,Tao, Dacheng,Li, Xuelong,&Murtagh, Fionn.(2009).Biologically Inspired Tensor Features.COGNITIVE COMPUTATION,1(4),327-341. |
| MLA | Mu, Yang,et al."Biologically Inspired Tensor Features".COGNITIVE COMPUTATION 1.4(2009):327-341. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Biologically Inspire（923KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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