Deep face recognition for dim images

Abstract

The performance of many state-of-the-art deep face recognition models deteriorates significantly for images captured under low illumination, mainly because the features of dim probe face images cannot match well with those of normal-illumination gallery images. The issue cannot be satisfactorily addressed by enhancing the illumination of face images and performing face recognition on the resulted images alone. We propose a novel deep face recognition framework that consists of a feature restoration network, a feature extraction network, and an embedding matching module. The feature restoration network adopts a two-branch structure based on the convolutional neural network to generate a feature image from the raw image and the illumination-enhanced image. The feature extraction network encodes the feature image into an embedding, which is then used by the embedding matching module for face verification and identification. The overall verification accuracy is improved from 1.1% to 6.7% when tested on the Specs on Faces (SoF) dataset. For face identification, the rank-1 identification accuracy is improved by 2.8%.