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A Survey of Face Recognition

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arxiv 2212.13038 v1 pith:Q7VI6MQG submitted 2022-12-26 cs.CV

Xinyi Wang , Jianteng Peng , Sufang Zhang , Bihui Chen , Yi Wang , Yandong Guo This is my paper · ORCID
classification cs.CV
keywords facerecognitiondeepdesignedindustrialsurveyalgorithmsanalyzed
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Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance

    cs.CV 2025-04 conditional novelty 5.0 of 10

    Fusing the outputs of four different 3D face reconstruction algorithms improves face verification on surveillance images, in both within-dataset and cross-dataset tests.

  2. Impact of Face Alignment on Face Image Quality

    cs.CV 2024-12 conditional novelty 5.0 of 10

    Aligning face images before quality assessment systematically raises FIQA scores, and the effect grows as image conditions worsen.

  3. Person detection and re-identification in open-world settings of retail stores and public spaces

    cs.CV 2025-05 reject novelty 2.0 of 10

    A demo of off-the-shelf person detection and re-identification on an OAK-D camera in retail and public spaces, with only qualitative results.

  4. CLIP Unreasonable Potential in Single-Shot Face Recognition

    cs.CV 2024-11 reject novelty 2.0 of 10

    Using a fine-tuned CLIP model on a 10-person dataset, the paper reports a lower false positive rate than VGG-Face and ArcFace, but the evaluation is too small and confounded to support a general conclusion.

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