Discriminative feature learning method and system for micro-expression recognition

Abstract

The invention discloses a discriminative feature learning method and system for micro-expression recognition. The method comprises the following steps: firstly, extracting a start frame and a peak frame in a micro-expression video sequence, preprocessing the start frame and the peak frame, and further calculating optical flow information between the peak frame and the start frame to obtain an optical flow graph; then selecting an image of which the expression category is different from that of the peak frame from the common expression image library, cutting the image, and replacing a corresponding region of the peak frame image with an image block obtained by cutting to obtain a composite image; constructing a double-flow convolutional neural network model based on a class activation graph attention mechanism, inputting the optical flow graph and the composite image into two branches of a double-flow convolutional neural network respectively, and training the model; and finally, extracting features with strong discriminability from an input video sequence by using the trained model for micro-expression classification and recognition. The method can effectively prevent the model from being over-fitted, enables the model to learn the micro-expression features with high discriminability, and improves the accuracy of micro-expression recognition.

Description

technical field

[0001] The invention relates to a discriminative feature learning method and system for micro-expression recognition, belonging to the field of micro-expression recognition and artificial intelligence. Background technique

[0002] Expression is a non-verbal behavior for humans to express their emotions, and it is also an important way for robots to intelligently understand human emotions. General facial expressions are shown by people when their emotional expression is not suppressed, and the range of facial movements is often large and lasts for a long time. But in some cases, people will deliberately suppress and hide their emotions, and these suppressed emotions will be expressed spontaneously through extremely fast facial expressions. This type of expression is called micro-expression. The duration of micro-expressions is extremely short, less than 0.2 seconds, and the changes in facial movements are so subtle that the accuracy of human recognition of m...

Examples

Embodiment Construction

[0083] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0084] Such as figure 1 As shown, a discriminative feature learning method for micro-expression recognition disclosed in the embodiment of the present invention specifically includes the following steps:

[0085] Step (1): Extract the start frame and peak frame of the video sequence samples in the micro-expression video library. The present embodiment uses the SMIC II database as a data source to extract the start frame and peak frame of each micro-expression video sequence sample, which specifically includes the following substeps:

[0086] (1.1) The first frame F of the micro-expression video sequence 1 , as the starting frame of the micro-expression image sequence;

[0087] (1.2) Set the total number of frames of the micro-expression video sequence as k, and subtract each frame image from the first frame image from the second frame to obt...

technical field

[0001] The invention relates to a discriminative feature learning method and system for micro-expression recognition, belonging to the field of micro-expression recognition and artificial intelligence. Background technique

[0002] Expression is a non-verbal behavior for humans to express their emotions, and it is also an important way for robots to intelligently understand human emotions. General facial expressions are shown by people when their emotional expression is not suppressed, and the range of facial movements is often large and lasts for a long time. But in some cases, people will deliberately suppress and hide their emotions, and these suppressed emotions will be expressed spontaneously through extremely fast facial expressions. This type of expression is called micro-expression. The duration of micro-expressions is extremely short, less than 0.2 seconds, and the changes in facial movements are so subtle that the accuracy of human recognition of m...

Examples

Embodiment Construction

[0083] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0084] Such as figure 1 As shown, a discriminative feature learning method for micro-expression recognition disclosed in the embodiment of the present invention specifically includes the following steps:

[0085] Step (1): Extract the start frame and peak frame of the video sequence samples in the micro-expression video library. The present embodiment uses the SMIC II database as a data source to extract the start frame and peak frame of each micro-expression video sequence sample, which specifically includes the following substeps:

[0086] (1.1) The first frame F of the micro-expression video sequence 1 , as the starting frame of the micro-expression image sequence;

[0087] (1.2) Set the total number of frames of the micro-expression video sequence as k, and subtract each frame image from the first frame image from the second frame to obt...

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