Face expression identification method and device

Abstract

The invention discloses a face expression identification method and device. The method comprises steps that a to-be-identified face image sequence is acquired, and the face image sequence comprises one or more than two face images; each face image of the face image sequence is pre-processed; the pre-processed face images are inputted to a trained training model to carry out expression identification to acquire expression identification results of the face image sequence; a convolutional neural network model, a long-time short-time memory cycle neural network model, a first pooling layer and a logic regression model are sequentially constructed from an input end to an output end of the training model, and the training model is acquired through training a continuous-image set marked with expression categories. The method is advantaged in that face expression identification performance can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a facial expression recognition method and a human facial expression recognition device. Background technique [0002] Facial expression recognition technology refers to assigning an expression category to a given face image, including: anger, disgust, happiness, sadness, fear, surprise, etc. At present, facial expression recognition technology has gradually shown broad application prospects in the fields of human-computer interaction, clinical diagnosis, distance education and investigation and interrogation, and is a popular research direction of computer vision and artificial intelligence. [0003] At present, there is a facial expression recognition method based on a deep convolutional neural network. After the face image is detected and calibrated, the calibrated human face image is input into a trained deep convolutional neural network. Expression recognition. I...

AI Technical Summary

Problems solved by technology

In the above-mentioned facial expression recognition method, the deep convolutional neural network is obtained through single-frame image training, and since the facial expression is closely related to the scene context and is very related to the neutral expression of the object, the above-mentioned facial expression The recognition method is difficult to accurately identify neutral expressions, and the recognition performance of facial expressions is poor

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