Effective micro-expression automatic identification method

Abstract

The invention discloses an effective micro-expression automatic identification method which comprises the steps of micro-expression frame sequence preprocessing, micro-expression information data study and micro-expression identification. The method for micro-expression frame sequence preprocessing comprises the steps that frames of obtained micro-expression sequences are detected, data of an image of each frame are extracted so that graying processing can be conducted on the data, and all the micro-expression sequences are interpolated into the frame of the unified number through the linear interpolation method. The method for micro-expression information data study comprises the steps that the micro-expression sequences obtained in the preprocessing stage are written in a tensor mode, then, the intra-class distance of the same class of micro-expressions is minimized in a tensor space through the discriminating analysis method of tensor expression and the between-class distance of different classes of micro-expressions is maximized, so that data dimension reduction is achieved, and characteristic data are ranked in a vectorized mode according to a class discriminating capacity descending order. A nearest neighbor classifier is used for micro-expression identification. Compared with the methods of MPCA, GTDA, DTSA and the like, the effective micro-expression automatic identification method has the advantages of being high in rate of identification, low in computer performance requirement and easy to achieve.

Description

technical field [0001] The invention belongs to the field of machine learning and pattern recognition, and relates to an effective micro-expression automatic recognition method, in particular to a micro-expression automatic recognition method using linear interpolation to normalize micro-expression samples and based on tensor expression discriminant analysis. Background technique [0002] The study of human facial expressions originated from Darwin in the 19th century [1] , recently, Ekman and Erika [2] A study of facial mapping behavior was conducted, validating that microexpressions can provide a more comprehensive disclosure of covert emotions. Micro-expression is a very fast expression that lasts only 1 / 25 second to 1 / 5 second. It expresses the real emotion that people seem to suppress and hide. It is often ignored by people, but it has an important application in lie detection prospect. [0003] In foreign countries, micro-expression recognition research has a histor...

AI Technical Summary

Problems solved by technology

In the existing methods, although MPCA can reduce the impact of noise, the low recognition performance restricts the reliability of this method. DTSA has high requirements for computer performance, and its recognition performance still cannot meet people's needs. Require

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