Face Quality Assessment Based on Local Gradient

Face recognition system performance is affected by face image quality. In this paper, a face matching score, instead of a subjective perceptual score, is used as the image quality label to build a face image quality database; therefore, the ground truth quality achieved high accordance with the recognition performance. The database contains five common practical application distortion types for 19 people and three to five collection scenes for each. In addition, a face image quality algorithm based on a local gradient is proposed to judge the image quality. Making use of the fact that face landmarks can locate contour areas that have rich gradient information, a new mask based on human visual characteristics is designed to evaluate the image quality by using the local gradient information of landmark neighborhoods; this method was used to control for the influence of the image content and background. Our algorithm does not rely on a large amount of training data, and it has low computational complexity. The experimental results show that the algorithm is effective and efficient for evaluating image quality and improving the performance of face recognition systems.