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...

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Problems solved by technology

In the existing methods, although MPCA can reduce the impact of noise, the low recognition performance restricts the reliability of t...

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