Low-quality video face recognition with deep networks and polygonal chain distance

Face recognition under surveillance circumstances still poses a significant problem due to low data quality. Nevertheless, automatic analysis is highly desired for criminal investigations due to the growing amount of security cameras worldwide. We suggest a face recognition system addressing the typical issues such as motion blur, noise or compression artifacts to improve low-quality recognition rates. A low-resolution adapted residual neural net serves as face image descriptor. It is trained by quality adjusted public training data generated by data augmentation strategies such as motion blurring or adding compression artifacts. To further reduce noise effects, a noise resistant manifold-based face track descriptor using a polygonal chain is proposed. This leads to a performance improvement on in-the-wild surveillance data compared to conventional local feature approaches or the state-of-the-art high-resolution VGGFace network.