Face image quality evaluation method and device and computer readable storage medium

Abstract

The invention relates to the technical field of image processing, particularly provides a face image quality assessment method and device and a computer readable storage medium, and aims to solve the problem of how to accurately assess the image quality of a face image so as to accurately perform face recognition. In order to achieve the purpose, the method comprises the steps of obtaining a face recognition model trained by different types of face image samples, and extracting image features of the same to-be-evaluated face image for multiple times through the face recognition model; respectively calculating a feature distance between every two image features and obtaining an average value of all feature distances obtained by calculation; predicting the probability that the to-be-evaluated face image belongs to the face image through a face recognition model according to the average value; and determining the face quality score of the to-be-evaluated face image according to the probability. Based on the above mode, the image quality of the face image can be accurately evaluated, so that face recognition can be accurately carried out.

Description

technical field [0001] The present invention relates to the technical field of image processing, and specifically provides a face image quality evaluation method, device and computer-readable storage medium. Background technique [0002] Face image quality assessment methods mainly include manual assessment methods and automatic assessment methods. The manual evaluation method mainly uses evaluators to evaluate the image quality of each face image one by one. Since this method completely relies on the subjective analysis of the evaluators, when using this method to evaluate the image quality of a large number of face images Not only time-consuming and laborious, but also prone to misjudgment. The automatic evaluation method mainly evaluates the quality of face images through image quality evaluation models with image quality evaluation capabilities, such as image quality evaluation models based on neural networks. However, this evaluation method is mainly based on conventio...

AI Technical Summary

Problems solved by technology

The manual evaluation method mainly uses evaluators to evaluate the image quality of each face image one by one. Since this method completely relies on the subjective analysis of the evaluators, when using this method to evaluate the image quality of a large number of face images Not only time-consuming and labor-intensive, but also prone to misjudgment

The automatic evaluation method mainly evaluates the quality of face images through image quality evaluation models with image quality evaluation capabilities, such as image quality evaluation models based on neural networks. However, this evaluation method is mainly based on conventional influences such as brightness and clarity. Image quality factors evaluate the quality of face images without considering the recognition effect of face recognition on face images

In practical applications, the high-quality images evaluated by the image quality evaluation model (such as images with brightness and clarity higher than the threshold value) are often unable to accurately and reliably evaluate these high-quality images due to reasons such as face occlusion and face pose. Therefore, these high-quality images are actually low-quality images that cannot perform accurate and reliable face recognition. It can be seen that this evaluation method cannot accurately evaluate the image quality of face images.

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