Praveen Kumar Chandaliya, Kiran RajaDamer, NaserGomez-Barrero, MartaRaja, KiranRathgeb, ChristianSequeira, Ana F.Todisco, MassimilianoUhl, Andreas2023-12-122023-12-122023978-3-88579-733-31617-5468https://dl.gi.de/handle/20.500.12116/43285Face image quality assessment is crucial 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 across both capture-related 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 extensively evaluate 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 combining multiple component scores into a unified score for broader application in face image enrolment in FRS.enBiometric sample qualityDatasetsEvaluationBenchmarkingText/Conference Paper