Face analysis method and system combining multi-task and multi-scale convolution neural network

Abstract

The invention discloses a face analysis method and a face analysis system integrating a multi-task combined multi-scale convolution neural network, Firstly, the key region search algorithm is used toextract K face regions of interest with different scales from a picture of N *N size, which is used as the input of three channels of multi-scale CNN. Then, CNN is used to extract the features of theK regions of interest to obtain the features of faces with different scales, and the extracted features of faces with different scales are fused in a cascade manner to obtain the fused feature expression; Then, the loss functions of a plurality of tasks are fused to obtain a joint loss function, and the feature expression is used as a learning input to obtain an optimal solution of the joint lossfunction, so as to obtain a processing result of the plurality of tasks. The invention utilizes the correlation between tasks to promote each other, and improves the prediction accuracy rate of a single task.

Description

technical field [0001] The present invention relates to the field of human face attributes, and more specifically, relates to a face analysis method and system that integrates multi-task and multi-scale convolutional neural networks. Background technique [0002] In recent years, with the development of the Internet and the achievements of artificial intelligence-related technologies in practical applications, the field of artificial intelligence has attracted the attention of more and more scientific researchers, and the application range of artificial intelligence technology has become wider and wider. In the field of computer vision, face detection and analysis has always been a hot research direction. Although the recent research based on deep convolutional neural network (CNN) has achieved remarkable results, and has been widely used in face recognition, face tracking, face detection and other fields, but for face detection tasks, It is still difficult to obtain facial...

AI Technical Summary

Problems solved by technology

Although the recent research based on deep convolutional neural network (CNN) has achieved remarkable results, and has been widely used in face recognition, face tracking, face detection and other fields, but for face detection tasks, It is still difficult to obtain facial keypoints, head pose, gender and expression information from face images containing extreme poses, lighting and resolution changes

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