Joint Image-to-Image Translation for Traffic Monitoring Driver Face Image Enhancement

Abstract

The real traffic monitoring driver face (TMDF) images are with complex multiple degradations, which decline face recognition accuracy in real intelligent transportation systems (ITS). This paper is the first to propose joint image-to-image (I2I) translation to enhance TMDF images of ITS. First, as TMDF images are without corresponding clear ones, identity preserving is critical for TMDF images under unpaired I2I translation. This paper proposes a fast diagonal symmetry pattern (FDSP) to preserve identity structure under unpaired I2I translation. Second, FDSP is introduced into CycleGAN to form FDSP-CG, which aims to learn the degradation mapping (i.e., FDSP-CG-d) from the clarity domain to the degradation domain. FDSP-CG-d can generate massive degradation/clarity image pairs for paired I2I translation training. Third, this paper proposes the dual residual block (DRB) to strengthen Pix2pix for rich face detail features learning (i.e., DRB-P2P), which learns the enhancement mapping from the degradation image to its clear version under paired I2I translation. Finally, the experiments on TMDF (i.e., the brevity name of the face database collected from real ITS) and Chinese famous face (CFF) databases, as well as CelebA and MegaFace databases, indicate that the proposed method can efficiently enhance TMDF images whose degradation variations are learned by FDSP-CG.