A Comprehensive Survey on Pose-Invariant Face Recognition

Abstract

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 paper, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, i.e., pose-robust feature extraction approaches, multi-view 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.

final version, ACM Transactions on Intelligent Systems and Technology, 2016

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• Robust Face Recognition via Multimodal Deep Face Representation
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Cited by in corpus (2)

• Robust Face Recognition via Multimodal Deep Face Representation
• Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition