Human face image collection method and device

Abstract

The embodiment of the invention provides a human face image collection method and device. The method comprises the steps: receiving a to-be-detected human face image; and evaluating the quality of the to-be-detected human face image through employing at least one type of trained depth convolution network. According to the invention, the method and device can accurately evaluate the quality of the to-be-detected human face image through employing the trained depth convolution network, thereby facilitating the improvement of the accuracy of human face recognition.

Description

technical field [0001] The present invention relates to the technical field of face recognition, and more particularly to a face image processing method and device. Background technique [0002] At present, face recognition technology is more and more widely applied to various scenes in life, including identity authentication in the account opening process in the financial field (such as banking and securities industries, etc.), VIP identification in commercial places, and suspect identification in the security field. Wait. The performance of face recognition technology has been greatly improved in recent years, and the recognition in non-extreme scenes can reach a level close to that of humans. [0003] However, when the face image quality is poor (for example, the side face angle is too large, motion blur or Gaussian blur exists in the image, etc.), the recognition rate and accuracy of face recognition will be significantly reduced. In the actual scene, it is found that ...

AI Technical Summary

Problems solved by technology

However, the face image quality assessment has not attracted enough attention. Some current face image quality assessment methods are usually too simple and rough, and the assessment results are not accurate enough, so it is difficult to greatly improve the performance of face recognition.

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