Real environment facial expression recognition method based on three-dimensional face feature reconstruction and image deep learning

Abstract

The invention discloses a real environment facial expression recognition method based on three-dimensional face feature reconstruction and graph deep learning. The method comprises the following steps: reconstructing and learning a network model 3DF-RLN through an established three-dimensional dense face reconstruction network and end-to-end trainable three-dimensional face features; obtaining facial appearance and facial geometric features through single 2D face image reconstruction, so that facial expression information is effectively represented. The facial topological graph based on the face key points is obtained through the facial geometric features, the correlation between the facial geometric features can be reflected, and the method has great significance in facial expression recognition and related research. The CNN network effectively extracts expression information contained in the facial appearance features, and the GCN network effectively extracts information contained in the facial geometric features. A fusion recognition module formed by channel attention and softmax effectively fuses complementary information contained in the facial appearance features and the facial geometric features, and the expression recognition accuracy is improved. In general, the method improves the accuracy of facial expression recognition, and improves the real environment facial expression recognition effect.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a real-environment facial expression recognition method based on three-dimensional face feature reconstruction and graph deep learning. Background technique [0002] In the prior art, facial expression recognition, as one of the important branches in the field of computer vision, has been successfully applied to telemedicine, fatigue driving monitoring, and many human-computer interaction systems. Many existing expression recognition methods have achieved remarkable success on expression databases in laboratory environments. However, due to the influence of arbitrary lighting, occlusion, and pose changes on facial expression images in real environments, it still poses a huge challenge to existing facial expression recognition methods. [0003] In recent years, with the advancement of face 3D reconstruction technology, the conversion from a single 2D facial i...

AI Technical Summary

Problems solved by technology

However, due to the influence of arbitrary lighting, occlusion, and pose changes on facial expression images in real environments, it still poses a huge challenge to existing facial expression recognition methods.

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