Multichannel convolutional neural network face expression recognition method based on attention mechanism fusion

Abstract

The invention relates to a multichannel convolutional neural network face expression recognition method based on attention mechanism fusion. The method comprises the steps of firstly detecting a faceregion from an input gray-scale image through a Viola-Jones face detector and rotation correction, and reducing the impact on the face expression recognition accuracy from an unrelated region as muchas possible, secondly, applying the detected face region to the depth image and the local binary pattern image to obtain three kinds of complementarity face region data, then, adopting a single-channel feature extraction network to automatically extract features related to expressions from the three types of face region data, sending the extracted features to an interactive attention fusion moduleto be fused, and enabling the module to extract spatial correlation of any two kinds of face region features based on an interactive attention mechanism, thereby realizing effective feature fusion ofdifferent types of face regions, and finally, after the features output by the interactive attention fusion module are spliced and fused again, conducting feature transformation through a full connection layer, and finally acquiring an expression recognition result through softmax operation.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a multi-channel convolutional neural network facial expression recognition method based on attention mechanism fusion. Background technique [0002] Face recognition is an important position for the application of computer vision technology. With the improvement of face recognition technology, facial expression recognition technology has received more and more attention. Facial expression recognition refers to the use of computer vision technology to predict the expressions of people in pictures containing human faces, generally referring to six basic expressions such as happiness, anger, sadness, fear, frustration, and surprise. This technology plays a great role in revealing people's emotions, intentions and other internal states. It is an important means for machines to perceive changes in human emotions and communicate with humans. widely used. [0003] The general ...

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Problems solved by technology

[0004] In recent years, facial expression recognition has made some progress, but as a subject with strong subjective factors, there are still the following difficulties in detection: (1) The same expression of different subjects may have large differences in appearance; (2) The appearance difference between different expressions of the same subject may not be obvious; (3) The same expression of the same subject may have large differences in appearance due to the influence of their emotional strength

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