Learning Race from Face: A Survey

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	Learning Race from Face: A Survey
	Fu, Siyao 1; He, Haibo 1; Hou, Zeng-Guang2
刊名	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	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
内容类型	期刊论文
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.