Learning to Attract and Repel: Dual Quality Margin Learning for Face Recognition (DQM-Face)
Read the original on arXiv Computer Vision →The paper introduces Dual Quality Margin Learning for Face Recognition (DQM‑Face), a framework that combines magnitude‑based and semantic quality estimation to refine attraction and repulsion dynamics during training. By integrating squeeze‑and‑excitation semantic attention with dual margins, the method enhances intra‑class compactness and inter‑class separation, yielding a more discriminative feature geometry. Experiments on challenging benchmarks show that DQM‑Face outperforms state‑of‑the‑art face recognition models and that the learned quality signal aligns well with recognition objectives.
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