FACE RECOGNITION UNDER LOW ILLUMINATION VIA DEEP FEATURE RECONSTRUCTION NETWORK

Recent major benchmarks show that deep-learning-based face recognition can achieve superb performance, even surpassing human capability. However, many state-of-the-art face recognition models suffer from severe performance degradation for images captured under low illumination. The issue can be addressed by enhancing the illumination of face images before performing face recognition. In this paper, we evaluate such enhancement methods and, based on the findings, propose a novel feature reconstruction network to make face features illumination-invariant by generating a feature image from both the raw face image and the illumination-enhanced face image. The performance of the proposed approach is tested on the Specs on Faces (SoF) dataset. The overall verification accuracy is improved by 0.5% to 2.5% and the rank-1 identification accuracy is improved by 2.1%.