RDCFace: Radial Distortion Correction for Face Recognition

The effects of radial lens distortion often appear in wide-angle cameras of surveillance and safeguard systems, which may severely degrade performances of previous face recognition algorithms. Traditional methods for radial lens distortion correction usually employ line features in scenarios that are not suitable for face images. In this paper, we propose a distortion-invariant face recognition system called RDCFace, which directly and only utilize the distorted images of faces, to alleviate the effects of radial lens distortion. RDCFace is an end-to-end trainable cascade network, which can learn rectification and alignment parameters to achieve a better face recognition performance without requiring supervision of facial landmarks and distortion parameters. We design sequential spatial transformer layers to optimize the correction, alignment, and recognition modules jointly. The feasibility of our method comes from implicitly using the statistics of the layout of face features learned from the large-scale face data. Extensive experiments indicate that our method is distortion robust and gains significant improvements on LFW, YTF, CFP, and RadialFace, a real distorted face benchmark compared with state-of-the-art methods.