Sharpness and Brightness Quality Assessment of Face Images for Recognition

Abstract

Face image quality has an important effect on recognition performance. Recognition-oriented face image quality assessment is particularly necessary for the screening or application of face images with various qualities. In this work, sharpness and brightness were mainly assessed by a classification model. We selected very high-quality images of each subject and established nine kinds of quality labels that are related to recognition performance by utilizing a combination of face recognition algorithms, the human vision system, and a traditional brightness calculation method. Experiments were conducted on a custom dataset and the CMU multi-PIE face database for training and testing and on Labeled Faces in the Wild for cross-validation. The experimental results show that the proposed method can effectively reduce the false nonmatch rate by removing the low-quality face images identified by the classification model and vice versa. This method is even effective for face recognition algorithms that are not involved in label creation and whose training data are nonhomologous to the training set of our quality assessment model. The results show that the proposed method can distinguish images of different qualities with reasonable accuracy and is consistent with subjective human evaluation. The quality labels established in this paper are closely related to the recognition performance and exhibit good generalization to other recognition algorithms. Our method can be used to reject low-quality images to improve the recognition rate and screen high-quality images for subsequent processing.