Micro-expression recognition method based on space-time appearance movement attention network

A technology of attention and micro-expressions, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve the problems of loss of effective information of micro-expressions, low recognition accuracy, ignoring contribution, etc., to reduce high-quality and large-scale requirements, low technical requirements, and the effect of reducing interference information
CN112307958AActive Publication Date: 2021-02-02HEBEI UNIV OF TECH +2
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
HEBEI UNIV OF TECH
Publication Date
2021-02-02

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Abstract

The invention relates to a micro-expression recognition method based on a space-time appearance movement attention network, and the method comprises the following steps: carrying out the preprocessingof a micro-expression sample, and obtaining an original image sequence and an optical flow sequence with a fixed number of frames; constructing a space-time appearance motion network which comprisesa space-time appearance network STAN and a space-time motion network STMN, designing the STAN and the STMN by adopting a CNN-LSTM structure, learning spatial features of micro-expressions by using a CNN model, and learning time features of the micro-expressions by using an LSTM model; introducing hierarchical convolution attention mechanisms into CNN models of an STAN and an STMN, applying a multi-scale kernel space attention mechanism to a low-level network, applying a global double-pooling channel attention mechanism to a high-level network, and respectively obtaining an STAN network added with the attention mechanism and an STMN network added with the attention mechanism; inputting the original image sequence into the STAN network added with the attention mechanism to be trained, inputting the optical flow sequence into the STMN network added with the attention mechanism to be trained, integrating output results of the original image sequence and the optical flow sequence through the feature cascade SVM to achieve a micro-expression recognition task, and improving the accuracy of micro-expression recognition.
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Description

technical field

[0001] The technical solution of the present invention relates to image data processing for micro-expression recognition, in particular to a micro-expression recognition method based on a spatio-temporal appearance motion attention network. Background technique

[0002] Micro-expressions are imperceptible facial expressions that a person tries to hide his true inner feelings but involuntarily reveal, which are fast, spontaneous, and unconscious. The duration of micro-expressions is short and the intensity is low, usually lasting 1 / 25s-1 / 5s, and the muscle movement caused by micro-expressions only appears in a small area of ​​the face, so it is difficult to correctly understand and recognize micro-expressions. To some extent, it limits the performance of micro-expression recognition. In recent years, a large number of algorithms using computer vision technology have emerged for automatic recognition of micro-expressions, which has greatly improved the applica...

Claims

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