Face video image quality optimization method, system and device

Abstract

The invention provides a face video image quality optimization method. The face video image quality optimization method comprises the steps of collecting a video sequence containing a face; extractinga target face image in the video sequence; and carrying out no-reference quality evaluation on the target face image; and extracting a target face image in the video sequence by using an inter-framedifference method. According to the method, non-reference quality evaluation is carried out on the video sequence images, quality scores are given to the images of the same identity, and screening ofthe high-quality images and filtering of the low-quality images are achieved.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to a method and system for optimizing the quality of human face video images. Background technique [0002] In video-based face recognition, usually multiple pictures of an identity in a video sequence are taken out for face recognition model training. A video sequence usually consists of multiple frames of face images taken in various complex and unrestricted environments. The images usually have different effects such as illumination, occlusion, blur, deflection angle, and focal length as the video frame changes. Therefore, all images of the same identity may include many poor-quality images such as uneven illumination, severe occlusion, blur, and out-of-focus. Directly using these images for face recognition model training will reduce the performance of the face recognition model. The use of better quality pictures for model training can effectively enhance the ...

AI Technical Summary

Problems solved by technology

Subjective evaluation is based on the observer's empirical quality score on the image quality, which depends on the observer's experience and standards, and the stability is poor

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