Evaluation of face image quality metrics in person identification problem

Face quality assessment algorithms play an important role in improving face recognition accuracy and increasing computational efficiency of biometric systems. In the case of video analysis system, it is very common to acquire multiple face images of a single person. Strategy for optimally choose of the face images with the best quality from the set of images should base on special quality metric. A set of face image quality metrics were investigated: image resolution, sharpness, symmetry, blur, measure of symmetry of landmarks points S, quality measure based on learning to rank. A new metric based on no-reference image quality assessment approach is proposed. For all the metrics the Spearman rank correlation coefficients with subjective expert assessment at different levels of face image scene illumination were calculated. The received results can help computer vison system engineers to optimize the biometric identification system.