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New dataset and framework tackle low-quality surveillance face recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the CVBL-CCTV-Face Dataset, designed to improve face enhancement and recognition in real-world surveillance scenarios. The dataset pairs low-quality images from CCTV cameras with high-quality reference images. A proposed framework utilizes face detection, deep learning for super-resolution, and an embedding model for identity recognition to address challenges like low resolution and motion blur. AI

IMPACT Enhances capabilities for AI-driven surveillance systems by improving face recognition accuracy in challenging conditions.

RANK_REASON The cluster describes a new dataset and framework for computer vision research, presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset and framework tackle low-quality surveillance face recognition

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The cluster describes a new dataset and framework for computer vision research, presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Manish Das, Ajay Kumar Yadav, Shiv Ram Dubey, Satish Kumar Singh ·

    CVBL-CCTV-Face Dataset for Real-World Face Enhancement and Recognition in Surveillance

    arXiv:2610.36781v1 Announce Type: new Abstract: In real-world surveillance settings, facial captures from closed-circuit television systems suffer from degradations such as low resolution, motion blur, noise, and occlusions, which undermine dependable face recognition. In this st…

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