Face image assessment learned with objective and relative face image qualities for improved face recognition
Hyung-Il Kim, Seung Ho Lee, Yong Man Ro · 2015
Considerable research efforts have been made for face recognition in various real-world applications. However, degraded face images, acquired in the real-world, make face recognition difficult. In this paper, we propose a new face image quality assessment that aims to realize a robust and reliable face recognition system. The proposed method considers two factors for face image quality, i.e., visual quality and mismatch between training and test face images. A face image quality assessor is learned based on the two factors to discriminate useful faces from unuseful ones. The proposed face image quality assessment model is robust and adaptive to face recognition systems by employing a learned assessment. Our experimental results on a challenging database show significant improvement in face recognition accuracy by the proposed method.