mlsanityData to 14 Oct 2026

EFaR 2023: Efficient Face Recognition Competition

arXiv 2308.04168 ↗First posted 8 Aug 2023cs.CVcited by 7

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Abstract (arXiv)

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 44 of its 71 references that are in the map. First posted on arXiv 8 Aug 2023.

Builds on

44 of 71 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 2.

7 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit Precision
  2. 2025UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition
  3. 2024OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations
  4. 2025Benchmarking Foundation Models for Zero-Shot Biometric Tasks
  5. 2026Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification
  6. 2023IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models
  7. 2025Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data
  8. 2026Benchmarking the Explanatory Quality of Open-Weight Vision-Language Models in Face Recognition