Investigating Cascaded Face Quality Assessment for Practical Face Recognition System
Recently, the development of practical face recognition (FR) system has received much attention. Despite of its extensive study, the FR performance could be severely degraded in real-life scenario (e.g., CCTV surveillance), due to uncontrolled face image conditions of pose/alignment, blur, and brightness. This paper proposes new automated face quality assessment (FQA) framework built-in to a practical FR system. In the proposed framework, three quality factors in face images are rapidly evaluated owing to a cascaded classification. Only face images that have been verified by the FQA are used in recognition phase. Our experiment shows that the cascaded FQA can successfully discard face images that could negatively affect FR.