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.
		Technical Report, 2018. Paper Bibtex 

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


Notice that the QMUL-SurvFace challenge is made available for research purposes. 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, comments, and evaluation results with a brief method description to Zhiyi Cheng at z.cheng@qmul.ac.uk.