QMUL SurvFace


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 face recognition benchmark to our best knowledge, where low-resolution face images are native and not synthesised by artificial down-sampling of native high-resolution images. This challenge contains 463,507 face images of 15,573 distinct identities captured in real-world uncooperative surveillance scenes across wide space and time. Face recognition is generally more difficult in an open-set setting which is typical for surveillance person search scenarios, owing to an arbitrarily large number of non-target people (distractors) appearing over open space and unconstrained time.



QMUL-SurvFace Dataset and Evaluation Codes (389MB): [Google Drive] [Baidu Cloud]


	    	Surveillance Face Recognition Challenge.
		Zhiyi Cheng, Xiatian Zhu and Shaogang Gong.
		arXiv:1804.09691, 2018. Bibtex Paper

We list below existing surveillance face recognition datasets. More extensive comparisons of face recognition datasets can be found in the paper.


Please notice that the QMUL-SurvFace challenge is made available for academic research purpose only. All the images were collected from the existing person re-identification datasets, and the copyright belongs to the original owners.


Please feel free to send any questions and/or comments to Zhiyi Cheng at z.cheng@qmul.ac.uk