Improved CNN-based facial expression recognition method

Abstract

The invention provides an improved CNN-based facial expression recognition method, and relates to the field of image classification and identification. The improved CNN-based facial expression recognition method comprises the following steps: s1, acquiring a facial expression image from a video stream by using a face detection alignment algorithm JDA algorithm integrating the face detection and alignment functions; s2, correcting the human face posture in a real environment by using the face according to the facial expression image obtained in the step s1, removing the background information irrelevant to the expression information and adopting the scale normalization; s3, training the convolutional neural network model to obtain and store an optimal network parameter before extracting feature of the normalized facial expression image obtained in the step s2; s4 loading a CNN model and the optimal network parameters obtained by s3 for the optimal network parameters obtained in the steps3, and performing feature extraction on the normalized facial expression images obtained in the step s2; s5, classifying and recognizing the facial expression features obtained in the step s4 by using an SVM classifier. The method has high robustness and good generalization performance.

Description

technical field [0001] The invention belongs to the field of image processing and pattern recognition, in particular to a facial expression recognition method based on an improved CNN. Background technique [0002] Human face Facial expression is one of the main non-verbal communication ways to express emotion and information, and it occupies 55% of the information. It can be seen that facial expressions, as an information carrier, play an important role in people's daily communication. Ekman et al. identified six human facial expressions (ie, anger, disgust, fear, happiness, sadness, and surprise) as universal basic human emotion expressions. Facial expression recognition has become a research hotspot in the field of computer vision for many years. Among them, the facial expression recognition system has a wide range of applications, such as human-computer interaction, developmental psychology, biology, medicine, information accessibility, and smart cities. It has a wide ...

AI Technical Summary

Problems solved by technology

Existing facial expression databases such as CK+, Japanese female facial expression (JAFFE) database, etc., the collected facial expression image samples are collected in an ideal environment and the original number is relatively small, and the training model is more likely to achieve overfitting

Therefore, although the current deep convolutional neural network model trained based on CK+ facial expression database has a relatively high accuracy rate (95%) in the test results, it does not mean that it is competent for the facial expression recognition task in the real environment.

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