Micro expression recognition method based on multi-feature multi-task dictionary sparse migration learning

Abstract

The invention relates to a micro expression recognition method based on multi-feature multi-task dictionary sparse migration learning. The micro expression recognition method comprises a training phase and a test phase. According to the micro expression recognition method provided by the invention, a macro expression and a micro expression are projected into a public space by means of projection,and in order to simplify the calculation and to improve the efficiency, sparse dictionary representation is performed on the projected data; in order to further reduce the data difference between thetwo domains, dictionaries of the two domains are reconstructed from each other to realize the relevance of the dictionaries, so that projected sparse representation matrixes generate greater correlation; in order to fully express the characteristics of the micro expression, in the micro expression recognition method provided by the invention, four kinds of different features are extracted for themicro expression, and the optimal combination is achieved by the selection of multiple features; and in order to highlight the detailed expression of the micro expression, the multi-task idea is imported by the micro expression recognition method provided by the invention to further enhance the recognition effect.

Description

technical field [0001] The invention relates to a micro-expression recognition method based on multi-feature multi-task dictionary sparse transfer learning, and belongs to the technical field of modal recognition and machine learning. Background technique [0002] Microexpressions are extremely short-lived, involuntary facial expressions that people reveal when they are suppressed or trying to hide their true emotions. This subtle expression was first discovered by Haggard and Issacs in 1966. Facial expressions in the traditional sense (we call them macro expressions) are more decorative. A normal adult can show his satisfactory expression after brain thinking, while micro-expression is inadvertently The expression revealed is the most real emotional leakage of a person, so it is difficult to pass the "screening" of the brain, so micro-expressions are more likely to reveal a person's real inner activities. Due to the characteristics of micro-expressions, it is considered a ...

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Problems solved by technology

[0005] At present, all kinds of micro-expression databases have the problem of small number and incomplete sample label information, so it is difficult to train an effective model. Considering that the traditional facial expression database has a large number of samples, the human face macro-expression and Establishing connections with micro-expressions and realizing knowledge transfer will help improve the recognition effect of micro-expressions

The application of transfer learning theory to the recognition of micro-expressions is still blank, and the transfer learning framework applied to other fields is not effective in the recognition of micro-expressions with small sample libraries and incomplete label information.

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