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A Survey on Face Recognition Systems

arXiv 2201.02991 ↗First posted 9 Jan 2022cs.CV cs.LGcited by 0

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Abstract (arXiv)

Face Recognition has proven to be one of the most successful technology and has impacted heterogeneous domains. Deep learning has proven to be the most successful at computer vision tasks because of its convolution-based architecture. Since the advent of deep learning, face recognition technology has had a substantial increase in its accuracy. In this paper, some of the most impactful face recognition systems were surveyed. Firstly, the paper gives an overview of a general face recognition system. Secondly, the survey covers various network architectures and training losses that have had a substantial impact. Finally, the paper talks about various databases that are used to evaluate the capabilities of a face recognition system.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 43 references that are in the map. First posted on arXiv 9 Jan 2022.

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References · 28

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16 more
  1. 2021WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognitioncited by 221
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  9. 2016A Comprehensive Analysis of Deep Learning Based Representation for Face Recognitioncited by 23
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  13. 2016Do We Really Need to Collect Millions of Faces for Effective Face Recognition?cited by 17
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  16. 2016Triplet Similarity Embedding for Face Verificationcited by 4

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