Abstract Face recognition (FR) is one of the most extensively investigated problems in computer vision. Recently we have witnessed significant progress in FR with the help of deep learning algorithms and larger scale datasets particularly those constrained social media web images, e.g. high-resolution photos of celebrity faces taken by professional photo-journalists. However, the more challenging FR in unconstrained and low-resolution surveillance images remains open and under-studied. To stimulate the development of innovative FR methods effective and robust for low-resolution surveillance facial images, we introduce a new Surveillance Face Recognition Challenge, namely QMUL-SurvFace. This benchmark is the largest to date and more importantly the only true surveillance FR benchmark to our best knowledge. The low-resolution facial images are not synthesised by artificial down-sampling of high-resolution imagery but drawn from real surveillance videos. This benchmark has 463,507 facial images of 15,573 unique identities captured in uncooperative surveillance scenes over wide space and time. Consequently, QMUL-SurvFace presents a true-performance surveillance FR challenge characterised by low-resolution, motion blur, uncontrolled poses, varying occlusion, poor illumination, and background clutters. On the QMUL-SurvFace challenge, we benchmark the FR performances of five representative deep learning face recognition models (DeepID2, CentreFace, VggFace, FaceNet, and SphereFace), in comparison to ex- isting benchmarks. We show that the current state of the arts are still far from being satisfactory to tackle the under-investigated surveillance FR problem. In particular, we demonstrate a surprisingly large gap in FR performance between existing celebrity photoshot benchmarks (e.g. MegaFace) and QMUL-SurvFace. For example, the CentreFace model only yields 29.9% in Rank-1 rate on QMUL-SurvFace, vs a much higher performance at 65.2% on MegaFace in a closed-set test. Besides, open-set FR is shown more challenging but required for surveillance scenarios due to the presence of a large number of non-target people (distractors) in public space. This is indicated by weak FR performances on QMUL-SurvFace, e.g. the top-performer CentreFace achieves 13.8% success rate (at Rank-20) at the 10% false alarm rate. Moreover, we extensively investigate the challenging low-resolution problem inherent to the surveillance facial imagery by testing different combining methods of image super-resolution and face recognition models on both web and surveillance datasets. Our evaluations suggest that current super-resolution methods are ineffective in improving low-resolution FR performance, although they have been shown useful to synthesise high frequency details for low-resolution web images. Finally, we discuss a number of open research directions and problems that need to be addressed for solving the surveillance FR challenge. The QMUL-SurvFace challenge is publicly available at https://qmul-survface. github.io/.
This complements the QMUL-SurvFace benchmark that is characterised by poor quality surveillance facial imagery captured from real-life security cameras deployed at open public spaces REF .
Surveillance Face Recognition Challenge
ArXiv
ArXiv
Computer Science