A personal identification-oriented face quality perception method and system. Prior knowledge that a human visual system evaluates image quality is learned from natural image quality evaluation tasks by using a meta-learning strategy, and a quality prior model obtained is finely adjusted by using a few face image samples to quickly obtain a face image identifiability perception model. The method and system extract face features by using a pre-trained VGGface model, and use an SVM classifier to learn the obtained face features to classify blocked faces and normal faces. The method and system can automatically perceive the identifiability and distortion type of a face, and provide a basis for selecting high-quality faces or selecting a face identification algorithm matching the distortion type.
PERSONAL IDENTIFICATION-ORIENTED FACE QUALITY PERCEPTION METHOD AND SYSTEM