Unified Face Image Quality Score Based on ISOIEC Quality Components

Faceimage quality assessment is a crucial task in the face enrolment process to obtain high-quality face images in the reference database. Neglecting quality control will adversely impact the accuracy and efficiency of face recognition systems, resulting in an image captured with poor perceptual quality. In this work, we present a holistic combination of 21 component quality measures proposed in “ISO/IEC CD 29794-5” and identify the varying nature of different measures across different datasets. The variance is seen both across capture-related measures and subject-related measures, which can be tedious for validating each component metric by a human observer when judging the quality of the enrolment image. Motivated by this observation, we propose an efficient method of combining quality components into one unified score using a simple supervised learning approach. The proposed approach for predicting face recognition performance based on the obtained unified face image quality assessment (FIQA) score was comprehensively evaluated using three datasets representing diverse quality factors. We further provide an extensive evaluation of the proposed approach using the Error-vs-Discard Characteristic (EDC) and show its applicability using five different FRS. The evaluation indicates promising results of the proposed approach in combining multiple component scores into a unified score for wider application in the context of face image enrolment in FRS.