PLGSA-Transformer: Periocular Landmark-Guided Attention with Occlusion-Adaptive Cosine Thresholding for Cross-Modal Masked and Unmasked Face Recognition
arXiv:2607.03581v1 Announce Type: cross Abstract: The widespread adoption of facial masks, accelerated by COVID-19 and mandated in security-sensitive settings, has exposed limitations of conventional face recognition systems. Existing approaches relying on fixed cosine thresholds, non-adaptive CNNs, and purely data-driven features fail to generalize when facial regions are occluded, creating a gap between lab performance and real-world deployability. This paper proposes PLGSA-Transformer, a cross-modal face matching framework with three contributions. First, Periocular Landmark-Guided Spatial
The increased prevalence of facial masks, driven by global health events and security mandates, has highlighted the limitations of existing face recognition technologies, creating an urgent need for more robust solutions.
This development indicates a significant advancement in biometric identification, directly addressing a critical vulnerability in security and public policy related to masked individuals.
Face recognition systems will become significantly more effective and reliable in real-world scenarios where faces are partially obscured, thus expanding their deployability and enhancing security applications.
- · Biometric security firms
- · Law enforcement agencies
- · Government security infrastructure
- · AI/Computer Vision researchers
- · Bad actors relying on masks for anonymity
- · Legacy face recognition system providers
- · Privacy advocates (potentially)
Improved accuracy of face recognition in masked scenarios will lead to broader adoption of these systems in public and private sectors.
Enhanced face recognition capabilities could lead to new debates around public surveillance and individual privacy rights.
The technology's success in overcoming occlusion challenges may pave the way for more resilient AI systems in other domains, like object recognition under partial visibility.
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Read at arXiv cs.AI