A Comprehensive Survey on Pose-Invariant Face Recognition

By Changxing Ding, Dacheng Tao. 2015

329 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Much work has focused on the face itself ([20, 21, 22, 23, 24, 25, 26, 27, 37] to name a few recent ones).”
Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition (2016, background): “Extracting high-quality face representations has always been a core task in face recognition [27].”
Robust Face Recognition via Multimodal Deep Face Representation (2015, background): “However, it remains a challenging task for multimedia applications, as observed in recent works [1], [2].”
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition (2017, methodology): “1) Pose-Invariant Face Recognition: According to [11], existing PIFR methods can be classified into four categories including: multi-view subspace learning [2], [28], pose-invariant feature extraction [6], [40], [42], face synthesis [19], [59], [65], and a hybrid approach of the above three [52], [5”
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition (2014, background): “Index Terms—Face recognition, face image descriptors, face image representation F”
Conditional Adversarial Generative Flow for Controllable Image Synthesis (2019, methodology): “This phenomenon results from that the underlying distribution of image conditions is difficult to measure precisely on the latent space, not to mention some multi-target 7984 978-1-7281-3293-8/19/$31.00 ©2019 IEEE DOI 10.1109/CVPR.2019.00818 tasks such as Pose-Invariant Face Recognition [6, 39] and ”
Person Recognition in Personal Photo Collections (2017, background): “11 DeepID2+ Face Recognition Module (hdeepid) [3] Face recognition performance have improved significantly in recent years with better architectures and larger open source datasets [3], [4], [5], [7], [8], [9], [10], [75], [76].”
Deep Face Recognition: A Survey (2018, background): “There have been several surveys on FR [268], [20], [3], [96], [175] and its subdomains, and they mostly summarized and compared a diverse set of techniques related to a specific FR scene, such as illumination-invariant FR [284], 3D FR [175], pose-invariant FR [262][50].”