Facial expression motion unit identification method based on space-time diagram convolutional network

A technology of convolutional network and motion unit, which is applied in the fields of computer vision, emotional computing, emotion recognition, and human-computer interaction. It can solve problems such as overfitting, small data samples, and data sets that cannot meet the needs of detection. Poor stickiness, the effect of improving accuracy
CN112633153AInactive Publication Date: 2021-04-09TIANJIN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TIANJIN UNIV
Publication Date
2021-04-09
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a facial expression motion unit identification method based on a space-time diagram convolutional network. The method comprises the steps: performing feature extraction on a facial motion unit AU area through a convolutional auto-encoder, then constructing a spatio-temporal relationship diagram of an AU sequence according to the AU spatio-temporal relationship closeness degree, and finally performing AU identification based on ST-GCN. According to the method, the facial motion unit is recognized based on the space-time diagram convolutional network, modeling is carried out on the space-time dependence relationship between the AUs by using the undirected space-time diagram model, and learning of AU deep representation features is carried out by using the space-time diagram convolutional network, so that the AU recognition accuracy is improved. The method can effectively solve the problems of poor robustness, low accuracy and the like of an AU detection model, and can be widely applied to expression analysis, emotion calculation and man-machine interaction application.
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Description

technical field

[0001] The present invention relates to the technical fields of computer vision and affective computing, and in particular to a human facial expression movement unit recognition (ActionUnit, AU) based on a Spatial-Temporal Graph Convolutional Networks (ST-GCN). It is widely used in emotion recognition, human-computer interaction and other fields. Background technique

[0002] Facial expressions can reveal people's inner activities, mental state, and social behaviors that are communicated outwards. With the development of artificial intelligence, human-centered facial expression recognition has gradually attracted widespread attention in the industry and academia. Expression analysis using facial motion coding system is one of the common methods for facial expression recognition.

[0003] The Facial Action Coding System (FACS) divides the human face into 44 facial movement units according to the movement of muscles from an anatomical point of view, which is ...

Claims

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