Expression recognition method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an expression recognition method and device, electronic equipment and a storage medium. The expression recognition model comprises a convolutional neural network model, a full connection network model and a bilinear network model. The method comprises steps of in the expression recognition process, preprocessing the to-be-identified image to obtain a face image and a key point coordinate vector; carrying out operation on the face image through a convolutional neural network model to output a first feature vector, performing operation on the key point coordinate vector through the full connection network model to output a second feature vector, performing operation on the first feature vector and the second feature vector through the bilinear networkmodel to obtain second-order information, and further obtaining an expression recognition result according to the second-order information. In the process, the prior expression information containedin the face key points is considered, the robustness to postures and illumination is good, and the expression recognition accuracy is improved. Furthermore, when the expression intensity is low, the expression can be correctly identified.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and in particular to an expression recognition method, device, electronic equipment, and storage medium. Background technique [0002] At present, facial expression recognition technology is widely used in fields such as human-computer interaction, assisted driving, distance education, and accurate advertisement placement. Expression recognition technology refers to the technology of obtaining and recognizing facial expressions from images or video sequences containing human faces. The basic facial expression categories are divided into 8 categories, namely anger (angry), contempt (contempt), disgust (disgust), fear (fear), happiness (happiness), sadness (sadness), surprise (surprise) and neutral ( neutral). [0003] In the existing expression recognition process, methods such as scale-invariant feature transform (SIFT) and histogram of oriented gradient (HOG) are...

AI Technical Summary

Problems solved by technology

In the recognition process, only the first-order information of the facial expression image is used, and no high-order feature information is used. The robustness to posture and illumination is crossed

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