Knowledge Guided Representation Disentanglement for Face Recognition from Low Illumination Images

Low illumination face recognition is challenging as details are lacking due to lighting conditions. Retinex theory points out that images can be divided into reflectance with color constancy and ambient illumination. Inspired by this, we propose a knowledge-guided representation disentanglement method to disentangle facial images into face-related and illumination-related features, and then leverage the disentangled face-related features for face recognition. Specifically, the proposed method consists of two components: feature disentanglement and face classifier. Following Retinex, high-dimensional face-related features and ambient illumination-related features are extracted from facial images. Reconstruction and crossreconstruction methods are used to make sure the integrity and accuracy of the disentangled features. Furthermore, we find that the influence of illumination changes on illumination-related features should be invariant for faces of different identities, so we design an illumination offset loss to satisfy the prior invariance for better disentanglement. Finally high-dimensional face-related features are mapped to low-dimensional features through the face classifier for use in face recognition task. Experimental results on low illumination and NIR-VIS datasets demonstrate the superiority and effectiveness of our proposed method.