Description: To facilitate more studies on developing face recognition models that are effective and robust for low-resolution surveillance facial images, we introduce a new Surveillance Face Recognition Challenge, which we call the QMUL-SurvFace benchmark. This new benchmark is the largest and more importantly the only true surveillance face recognition benchmark to our best knowledge, where low-resolution images are not synthesised by artificial down-sampling of native
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QMUL-SurvFace: Surveillance Face Recognition Challenge
Zhiyi Cheng Xiatian Zhu Shaogang Gong Computer Vision Group, School of Electronic Engineering and Computer Science, Queen Mary University of London Home Protocols Leaderboard Description To facilitate more studies for developing face recognition methods that are effective and robust against low-resolution surveillance facial images, a new Surveillance Face Recognition challenge, QMUL-SurvFace , is introduced. This new challenge is the largest and more importantly the only true surveillance f
May 28, 2019: We are organizing the QMUL-SurvFace: Surveillance Face Recognition Challenge in conjunction with the ICCV'19 Workshop and Challenge on Real-World Recognition from Low-Quality Images and Videos (RLQ) . August 29, 2018: Updated the evaluation results of the state-of-the-art face recognition methods. June 01, 2018: QMUL-SurvFace dataset, the evaluation protocol, and test codes are released.