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Facial micro-expression recognition method

A recognition method and micro-expression technology, applied in the field of facial micro-expression recognition, can solve the problem of insufficient micro-expression samples, and achieve the effect of improving the recognition accuracy

Active Publication Date: 2018-02-09
UNIV OF SCI & TECH BEIJING
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a human face micro-expression recognition method to solve the problem of insufficient micro-expression samples in the prior art

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Embodiment Construction

[0039] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0040] Aiming at the problem of insufficient micro-expression samples in the prior art, the present invention provides a face micro-expression recognition method.

[0041] Such as figure 1 As shown, the face micro-expression recognition method provided by the embodiment of the present invention includes:

[0042] S101: Obtain the first training set from the macro expression database to pre-train the 3D convolutional neural network, and save the pre-trained network model;

[0043] S102: Obtain a second training set from the micro-expression database, adjust the pre-trained network model, and extract feature vectors from the last fully connected layer of the 3D convolutional neural network, and input them into the linear SVM classifier for trai...

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Abstract

The invention provides a facial micro-expression recognition method which can improve the recognition accuracy of micro-expressions. The method includes: acquiring a first training set from a macro expression database to pre-train a 3D convolutional neural network (CNN) and storing a pre-trained network model; acquiring a second training set from a micro-expression database, adjusting the pre-trained network model and extracting a feature vector from the last fully connected layer of the 3D CNN, inputting the feature vector into a linear SVM classifier for training; inputting a to-be-tested image, extracting a feature vector from the last fully connected layer of the 3D CNN and inputting the feature vector into the linear SVM classifier for classification. The present invention relates tothe technical field of image processing and pattern recognition.

Description

Technical field [0001] The invention relates to the technical field of image processing and pattern recognition, in particular to a method for identifying human facial micro-expression. Background technique [0002] Facial expression recognition is a hot research field that has received extensive attention in recent years. Although the research on facial expression recognition began in the 1970s, the research on facial micro-expressions is still relatively small, mainly because people are good at distinguishing facial expressions with rich facial movements rather than facial expressions with small changes. Micro-expression is an extremely short-lived facial expression with small movements, usually lasting no more than 0.5 seconds. Due to these characteristics of micro-expression, it has a wide range of fields of polygraph, clinical diagnosis, education and criminal investigation. Application prospects. [0003] In recent years, deep learning has increasingly demonstrated its pote...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/175G06N3/045G06F18/2411G06F18/214
Inventor 支瑞聪许海瑞
Owner UNIV OF SCI & TECH BEIJING
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