# A Comprehensive Survey on Pose-Invariant Face Recognition

arXiv: https://arxiv.org/abs/1502.04383 · HTML: https://mlsanity.com/paper/1502.04383/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2015.

## Built on by (most cited first, 9 of 9)

- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Assessing Dataset Bias in Computer Vision](https://mlsanity.com/paper/2205.01811/) (2022)
- [Pose Attention-Guided Profile-to-Frontal Face Recognition](https://mlsanity.com/paper/2209.07001/) (2022)
- [Attention-guided Progressive Mapping for Profile Face Recognition](https://mlsanity.com/paper/2106.14124/) (2021)
- [Protego: User-Centric Pose-Invariant Privacy Protection Against Face Recognition-Induced Digital Footprint Exposure](https://mlsanity.com/paper/2508.02034/) (2025)
- [Human Pose Transfer with Augmented Disentangled Feature Consistency](https://mlsanity.com/paper/2107.10984/) (2021)
- [Human Face Recognition from Part of a Facial Image based on Image Stitching](https://mlsanity.com/paper/2203.05601/) (2022)
- [Face Clustering for Connection Discovery from Event Images](https://mlsanity.com/paper/2309.01092/) (2023)
- [Markerless Head Tracking for Accurate and Accessible Neuronavigation](https://mlsanity.com/paper/2602.07052/) (2026)

## Abstract (arXiv)

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.