Tensor decomposition-based multi-feature fusion 4D expression recognition method

Abstract

The invention discloses a tensor decomposition-based multi-feature fusion 4D expression recognition method. The method comprises the steps of obtaining human face 4D expression data; preprocessing thehuman face 4D expression data, and calculating to obtain three normal vector components, a shape index and a depth map of the 4D human face data; tensor decomposition is carried out on three normal vector components, the shape index and the depth map of the 4D face data, and dynamic face expression information is extracted; and classifying the dynamic facial expression information by using a dynamic image network, and performing score fusion on a classification result to obtain a final classification result. According to the method, the information of the 4D face data is fully utilized, the three components, the shape index and the depth map of the normal vector of the face are calculated for the sequence face data, the 3D geometrical information of the face is fully utilized, for different people, the features of the face are more representative and discriminative, and the accuracy of face recognition and expression recognition is higher.

Description

technical field [0001] The invention relates to an expression recognition method, in particular to a 4D expression recognition method based on multi-feature fusion of tensor decomposition. Background technique [0002] With the development and progress of artificial intelligence and computer technology, expression recognition and face recognition have attracted more and more attention. Expression recognition and face recognition are widely used in daily life. There are many current facial expression recognition methods, for example, using a deep neural network to extract the features of 2D pictures or videos, and then classify them. There are also expression classifications using 3D face data. In fact, 2D-based facial expression recognition is easily affected by lighting and scenes. 3D-based expression recognition can overcome the influence of lighting and posture, but for 3D expression recognition, because different people express expressions in different ways and degree...

AI Technical Summary

Problems solved by technology

In fact, 2D-based facial expression recognition is easily affected by lighting and scenes

3D-based expression recognition can overcome the influence of lighting and posture, but for 3D expression recognition, because different people express expressions in different ways and degrees, even with the same expression, different individuals vary widely.

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