Method for recognizing human face micro-expressions in video sequence

Abstract

The invention discloses a method for recognizing human face micro-expressions in a video sequence and relates to the treatment on a recording carrier for recognized patterns. A method for the extracting dynamic spatial-temporal textural characteristics of human face micro-expression sequences based on the HLACLF-TOP algorithm is provided. The method comprises the steps of amplifying the video of human face micro-expressions based on the Euler function, preprocessing the images of human face micro-expressions, extracting the dynamic spatial-temporal textural characteristics of human face micro-expression sequences based on the HLACLF-TOP algorithm, training and predicting by utilizing an ELM classifier. Based on the above method, the defect in the prior art that human face micro-expressions are hard to recognize due to the small variation amplitude of human face micro-expressions can be overcome.

Description

technical field [0001] The technical solution of the present invention relates to the processing of a record carrier for recognizing graphics, in particular to the recognition method of human facial micro-expressions in a video sequence. Background technique [0002] Micro-expression (micro-expression) is a very short-lived uncontrollable facial expression revealed by humans when they try to suppress or hide their true emotions. Ekman et al. conducted a series of studies on micro-expression, and the results showed that micro-expression is an effective way to identify lies. Clues can be widely used in security, judicial, clinical and legal fields. However, micro-expressions are short-lived and difficult to recognize. Even with well-trained people, the accuracy rate of micro-expression recognition is only about 40%. Therefore, it is very necessary to develop an automatic micro-expression recognition system and realize computer automatic recognition of micro-expressions, both ...

AI Technical Summary

Problems solved by technology

CN103258204A discloses a kind of automatic micro-expression recognition method based on Gabor and EOH feature, utilizes Gabor and EOH feature to carry out micro-expression feature extraction, but the ability of Gabor and EOH to represent the whole situation is weak, and needs to be combined with the improved GentleSVM classifier to carry out The classification and recognition of micro-expressions requires high performance requirements of hardware equipment, but the recognition performance is lower than people's expectations

CN104298981A proposes an automatic micro-expression recognition method based on CBP-TOP features, and uses an ELM classifier to classify. This method involves multiplication and power operations when calculating the CBP value of each pixel. Compared with the method of the present invention, the calculation The complexity is high, and the extracted micro-expression information is not complete

Existing face micro-expression recognition methods mainly have the defects of low recognition performance due to the fact that the extracted micro-expression sequence information is single and does not consider multiple frequencies and directions.

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