A face multi-area fusion expression recognition method based on depth learning

Abstract

The invention discloses a face multi-area fusion expression recognition method based on depth learning, which comprises the following steps of detecting a face position with a detection model; obtaining the coordinates of the key points by using the key point model; aligning the eyes according to the key points of the eyes, then aligning the face according to the coordinates of the key points of the whole face, and clipping the face region by affine transformation; cutting the eye and mouth areas of the image to a certain proportion; dividing the convolution neural network into one backbone network and two branch networks; carrying out the feature fusion in the last convolution layer, and finally obtaining the expression classification results by the classifier. The method of the inventionutilizes the priori information, besides the whole face, the eyes and mouth regions are also used as the input of the network, and the network can learn the whole semantic features of facial expressions and the local features of facial expressions through model fusion, so that the method simplifies the difficulty of facial expression recognition, reduces the external noise, and has strong robustness, high accuracy, low complexity of the algorithm and so on.

Description

technical field [0001] The invention relates to the technical fields of computer vision and pattern recognition, in particular to a deep learning-based facial multi-region fusion expression recognition method. Background technique [0002] The face multi-region fusion expression recognition method based on deep learning is a kind of facial expression recognition, and its purpose is to solve the problem of facial expression classification. [0003] In 1971, psychologists Ekman and Friesen proposed six basic human emotions, namely Surprise, Sadness, Anger, Fear, Disgust and Happiness. Correspondingly, humans can produce corresponding facial expressions. Human expressions often carry richer information than language. Therefore, facial expression recognition is an important research topic in the field of computer vision. Its research results can be applied to the fields of human-computer interaction, treatment of patients with mental illness, affective computing and distance e...

AI Technical Summary

Problems solved by technology

[0006] The problem of facial expression classification has always attracted the attention of scholars at home and abroad, especially for the task of facial expression recognition in real scenes, which is very challenging.

Because facial expressions in real scenes are spontaneously generated, they are very different from samples collected in most laboratories; at the same time, problems such as large postures, large occlusions, uneven light, uneven picture quality, and indistinct expression features increase the It is more difficult to identify

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