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.
Highlights
Extensive research on face image quality (FIQ) has shown that samples given as inputs to an automated recognition system influence recognition performance
Filtering low-quality images by face image quality assessment (FIQA) is an important way to improve the performance of recognition systems
Face Databases and Preprocessing. is work utilized three face databases: the multi-PIE [12] face database (M-PIE), a self-built SC database, and the Labeled Faces in the Wild (LFW). e M-PIE was collected under an environment with strict lighting, posture, and expression control in four sessions over a five-month period; these data consist of 337 subjects and more than 750,000 highresolution face images. e SC database consists of approximately 5000 face images of 945 subjects selected from identification channels of Wisesoft Co., Ltd. e subjects were employees of the company and agreed to the use of their images in the study. e specific screening methods will be described later. e images in LFW were derived from natural scenes in life, and a total of 13,233 images of
Summary
Extensive research on face image quality (FIQ) has shown that samples given as inputs to an automated recognition system influence recognition performance. Face recognition has been increasingly applied in uncontrollable environments (e.g., automated security checkpoints) where the acquired images may include blur, uneven illumination, and nonfrontal poses. Such nonideal factors can significantly decrease the recognition accuracy. E most direct manifestation of this decreased accuracy is that the face recognition performance of the same recognition algorithm on datasets with different qualities has obvious differences. Cao et al [2] proposed a posture robustness recognition algorithm, and Fekri-Ershad [3] classified face gender to help improve the recognition rate. Filtering low-quality images by face image quality assessment (FIQA) is an important way to improve the performance of recognition systems
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