Performance Face Image Quality Assessment under the Difference of Illumination Directions in Face Recognition System using FaceQnet, SDD-FIQA, and SER-FIQ

Abstract

The Face Recognition system has a challenge when the conditions of the input images have differences in quality from the images that have been enrolled in the database. One of the causes is the variation in lighting that causes illumination in image. We used images with normal lighting, as well as four images that have variations in the lighting/illumination directions. Face Image Quality Assessment (FIQA) helps the face recognition system to ensure the optimum captured image quality for enrollment and verification process. We use both supervised (FaceQnet) and unsupervised (SDD-FIQA, SER-FIQ) FIQA method against the Asian Face Image dataset. The result shows that filtering images using FIQA method can reduce FNMR by 58.89% in matching images whose light direction is from below. Images with type 2 illumination, where an image whose light comes from below matched with normal image, gave the lowest result in FRR compared to other types of illumination when tested with 3 FIQA methods.