期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 7, 期 3, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2845089
关键词
Algorithms; Performance; Security; Pose-invariant face recognition; pose-robust feature; multiview learning; face synthesis; survey
资金
- Australian Research Council [FT-130101457, DP-140102164]
The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, Pose-Invariant Face Recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this article, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed.
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