Micro-expression recognition method based on active migration learning

A technology of transfer learning and recognition methods, applied in the field of machine learning and pattern recognition, can solve the problem of small number of database samples and achieve the effect of improving the effect
CN108629314AActive Publication Date: 2018-10-09SHANDONG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANDONG UNIV
Publication Date
2018-10-09

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Abstract

The invention relates to a micro-expression recognition method based on active migration learning. The method comprises the following steps: (1) extracting micro-expression and micro-expression features; (2) establishing and solving a micro-expression active migration learning problem; (3) recognizing a microexpression. Based on the internal relation between macro expressions and micro expressions, a bridge between macro expressions and micro expressions is constructedthrough an asymmetric linear translator. Less labeled samples are adopted during the initial stage of the active learning. By means of the above translator, the supervision information of the macro-expression domain can be utilized by micro expressions in thetransformation domain. As a result, a high-quality sample can be selected for the active learning in the micro-expression domain. After the sample is manually marked, the sample is added to an existing training set. Therefore, a more effective classifier is obtained through training.
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Description

technical field

[0001] The invention relates to a micro-expression recognition method based on active transfer learning, which belongs to the technical field of machine learning and pattern recognition. Background technique

[0002] Micro-expression is a type of spontaneous expression. It is a natural expression of emotion and an inadvertent expression of emotion, so it cannot be copied and forged. Therefore, it has great application value in the fields of clinical diagnosis and safety precautions. The labeling of micro-expression categories is a demanding task, which requires researchers with a psychological background to combine the context information of the image sequence to give a comprehensive judgment. In order to give standard micro-expression samples, researchers have spent a lot of time and experience designing induction mechanisms, collecting samples and manually labeling, but the number of existing database samples is still too small.

[0003] Macro-expressions,...

Claims

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