mlsanityData to 7 Oct 2026

Going Deeper Into Face Detection: A Survey

arXiv 2103.14983 ↗First posted 27 Mar 2021cs.CVcited by 10

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Abstract (arXiv)

Face detection is a crucial first step in many facial recognition and face analysis systems. Early approaches for face detection were mainly based on classifiers built on top of hand-crafted features extracted from local image regions, such as Haar Cascades and Histogram of Oriented Gradients. However, these approaches were not powerful enough to achieve a high accuracy on images of from uncontrolled environments. With the breakthrough work in image classification using deep neural networks in 2012, there has been a huge paradigm shift in face detection. Inspired by the rapid progress of deep learning in computer vision, many deep learning based frameworks have been proposed for face detection over the past few years, achieving significant improvements in accuracy. In this work, we provide a detailed overview of some of the most representative deep learning based face detection methods by grouping them into a few major categories, and present their core architectural designs and accuracies on popular benchmarks. We also describe some of the most popular face detection datasets. Finally, we discuss some current challenges in the field, and suggest potential future research directions.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 57 of its 99 references that are in the map. First posted on arXiv 27 Mar 2021.

Builds on

57 of 99 references.

References · 57

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38 more
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50 most cited of 57 All 57 (JSON)

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Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 0; 2025: 2.

10 papers.

  1. 2021InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wildcited by 9
  2. 2023Racial Bias within Face Recognition: A Surveycited by 9
  3. 2023DisguisOR: Holistic Face Anonymization for the Operating Roomcited by 9
  4. 2022Robustness Disparities in Face Detectioncited by 5
  5. 2022Are Commercial Face Detection Models as Biased as Academic Models?cited by 3
  6. 2022Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacycited by 2
  7. 2023Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterioncited by 2
  8. 2025B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM Pruningcited by 2
  9. 2022Equalization and Brightness Mapping Modes of Color-to-Gray Projection Operatorscited by 0
  10. 2025SoK: On the Survivability of Backdoor Attacks on Unconstrained Face Recognition Systemscited by 0