Learning Race from Face: A Survey
文献类型：期刊论文
| 作者 | Fu, Siyao1; He, Haibo1; Hou, Zeng-Guang2
|
| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2014-12-01 |
| 卷号 | 36期号:12页码:2483-2509 |
| 关键词 | Race classification face recognition image categorization data clustering face database machine learning computer vision |
| 英文摘要 | Faces convey a wealth of social signals, including race, expression, identity, age and gender, all of which have attracted increasing attention from multi-disciplinary research, such as psychology, neuroscience, computer science, to name a few. Gleaned from recent advances in computer vision, computer graphics, and machine learning, computational intelligence based racial face analysis has been particularly popular due to its significant potential and broader impacts in extensive real-world applications, such as security and defense, surveillance, human computer interface (HCI), biometric-based identification, among others. These studies raise an important question: How implicit, non-declarative racial category can be conceptually modeled and quantitatively inferred from the face? Nevertheless, race classification is challenging due to its ambiguity and complexity depending on context and criteria. To address this challenge, recently, significant efforts have been reported toward race detection and categorization in the community. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, and applications. We first discuss race perception problem formulation and motivation, while highlighting the conceptual potentials of racial face processing. Next, taxonomy of feature representational models, algorithms, performance and racial databases are presented with systematic discussions within the unified learning scenario. Finally, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potentially important cross-cutting themes and research directions for the issue of learning race from face. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | DEVELOPMENTAL INTERGROUP THEORY ; FACIAL EXPRESSIONS ; RECOGNITION ALGORITHMS ; COMPUTER VISION ; PERCEPTUAL DISCRIMINATION ; ETHNICITY IDENTIFICATION ; SELECTIVE ATTENTION ; AMYGDALA ACTIVITY ; SOCIAL COGNITION ; VISUAL-ATTENTION |
| 收录类别 | SCI ; SSCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000344988000012 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3481]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 作者单位 | 1.Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fu, Siyao,He, Haibo,Hou, Zeng-Guang. Learning Race from Face: A Survey[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2014,36(12):2483-2509. |
| APA | Fu, Siyao,He, Haibo,&Hou, Zeng-Guang.(2014).Learning Race from Face: A Survey.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,36(12),2483-2509. |
| MLA | Fu, Siyao,et al."Learning Race from Face: A Survey".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 36.12(2014):2483-2509. |
入库方式： OAI收割
来源：自动化研究所
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