学习吸引与排斥：面向人脸识别的双质量间隔学习（DQM-Face）
Learning to Attract and Repel: Dual Quality Margin Learning for Face Recognition (DQM-Face)
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中文总结 AI 辅助
针对非受控人脸识别受噪声干扰导致判别性不足的问题，提出DQM-Face框架，结合幅值与语义质量建模，通过双吸引-排斥间隔优化提升性能，在多个基准上优于现有方法，且学习到的质量可用于人脸图像质量评估。
中文摘要 AI 辅助
在非受控环境下的人脸识别任务中，由于真实场景中存在多样且极端的变化，该任务仍然极具挑战性。为缓解这些影响，现有的基于间隔的方法通过特征幅值来建模样本质量。然而，仅依靠幅值进行建模易受与身份无关的噪声干扰，这会降低学习到的表征的可靠性和判别能力。本文提出了面向人脸识别的双质量间隔学习（DQM-Face），这是一种能在表征学习过程中实现精细吸引与排斥动态的新型框架。我们的方法将传统的基于幅值的质量估计与新引入的语义质量学习机制相统一，该机制通过挤压激励语义注意力实现。通过联合利用幅值和语义线索，我们构建了增强的质量感知间隔，该间隔在学习过程中通过改进的吸引作用自适应地增强类内紧凑性。为进一步增强类间判别性，我们引入了一种排斥间隔公式，该公式明确扩大了类间间隔。语义质量建模与双吸引-排斥间隔优化的统一集成，形成了更具结构化和判别性的特征几何结构。在多个具有挑战性的基准上进行的大量实验表明，DQM-Face始终优于当前最先进的人脸识别方法。此外，我们还证明，在该框架中，为间隔优化学习到的质量对于人脸图像质量评估也非常有效，这表明学习到的质量信号与识别目标内在一致。代码已公开：this https URL
英文摘要
Face recognition in unconstrained environments remains highly challenging due to diverse and extreme variations encountered in real-world scenarios. To mitigate these effects, existing margin-based approaches model sample quality through feature magnitude. However, magnitude-based modeling alone is susceptible to identity-agnostic noise, which can degrade the reliability and discriminative power of learned representations. In this paper, we propose Dual Quality Margin Learning for Face Recognition (DQM-Face), a novel framework that enables refined attraction and repulsion dynamics during representation learning. Our approach unifies conventional magnitude-based quality estimation with a newly introduced semantic quality learning mechanism, realized via squeeze-and-excitation semantic attention. By jointly leveraging magnitude and semantic cues, we construct enhanced quality-aware margins that adaptively strengthen intra-class compactness through improved attraction during learning. To further enhance inter-class discrimination, we introduce a repulsion margin formulation that explicitly enlarges inter-class separation. The unified integration of semantic quality modeling with dual attraction-repulsion margin optimization results in a more structured and discriminative feature geometry. Extensive experiments on multiple challenging benchmarks demonstrate that DQM-Face consistently outperforms state-of-the-art face recognition methods. Moreover, we show that the quality learned for margin optimization is highly effective for face image quality assessment within the proposed framework, demonstrating that the learned quality signal is intrinsically aligned with the recognition objective. The code is publicly available: https://github.com/RAIB-group/DQM-Face
发表机构
- University of Sciences and Technology Houari Boumediene (USTHB)（侯阿里·布迈丁科技大学）
- Johannes Gutenberg University Mainz（美因茨约翰内斯·古腾堡大学）
机构由 AI 辅助整理，请以论文原文为准。
