Deep convolutional neural network-based human face image quality prediction method

Abstract

The invention provides a deep convolutional neural network-based human face image quality prediction method. The method comprises the main steps of building a human face image quality prediction model; creating a human face image quality tag; and automatically predicting human face image quality. According to the process, a support vector regression model is trained by using a deep convolutional neural network (ConvNet); facial features are extracted for predicting facial image quality; a large database of unrestricted facial images is established; image features are extracted by using the deep ConvNet; and the human face image quality is predicted from the features extracted by the ConvNet through adopting the support vector regression model. According to the method, clustering is performed by comparing paired facial images; through matrix solving, a complete quality level is derived; and by depending on a supervised learning technology, the facial image target quality tag is created,so that the human face image quality prediction accuracy is improved, and a further contribution is made for new design of engineering field and innovative solutions of monitoring field.

Description

technical field [0001] The invention relates to the field of face image quality detection, in particular to a method for predicting the quality of face images based on a deep convolutional neural network. Background technique [0002] In face recognition, since the quality of the collected face images is difficult to ensure consistency, and even blurry and other degradations may occur, it will affect the accurate recognition of faces, so it is necessary to predict and evaluate the quality of face images. To reduce the impact of image quality on recognition performance. The face image quality prediction method can not only accurately evaluate the quality of the face image, but also has a high speed, which can meet the real-time requirements of the face recognition system, and can be conveniently used as a preprocessing method for the automatic face recognition system . Face image quality prediction plays a very important role in traditional image processing fields, such as ...

AI Technical Summary

Problems solved by technology

The performance of an automatic face recognition system depends largely on the quality of the facial images acquired, however, there are many emerging facial recognition applications that, as they seek to capture facial images under less than ideal conditions, most of the images The face changes greatly, which significantly reduces the accuracy of face recognition

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