Cross-library micro-expression recognition method and device based on optical flow attention neural network

Abstract

The invention discloses a cross-database micro-expression recognition method and a device based on an optical flow attention neural network. The method comprises the following steps: (1) acquiring twodifferent micro-expression databases as a training set and a test set; (2) converting the micro-expression video into a face image sequence; (3) extracting a starting frame, a peak frame and a termination frame from each face image sequence, calculating the starting frame and the peak frame to obtain a first single-channel optical flow graph, and calculating the peak frame and the termination frame to obtain a second single-channel optical flow graph; (4) forming a fusion feature graph by the first single-channel optical flow graph, the second single-channel optical flow graph and the peak frame of each face image sequence; (5) establishing an optical flow attention neural network, and taking the fusion feature graphs corresponding to the training set and the test set as input for training; and (6) processing the micro-expression video to be identified to obtain a fusion feature map, and inputting the fusion feature map into the optical flow attention neural network to obtain a micro-expression category. The method is high in generalization capability and high in recognition accuracy.

Description

technical field [0001] The invention relates to image processing technology, in particular to a cross-database micro-expression recognition method and device based on an optical flow attention neural network. Background technique [0002] Micro-expressions are short-lived facial expressions that humans make unconsciously when trying to hide an emotion. Micro-expressions are an important real emotional information, which can usually effectively reflect a person's real psychological state. Therefore, the effective and accurate identification of micro-expressions is of great significance to daily production and life. For example, in criminal investigation, interrogators trained in micro-expression recognition can more effectively judge the authenticity of a suspect’s words and obtain reliable information in a more targeted manner; in clinical medicine, doctors can use micro-expression recognition to effectively infer a patient’s real-world status to communicate more effective...

AI Technical Summary

Problems solved by technology

[0005] Traditional micro-expression recognition is often trained and tested on a single micro-expression database, and the data of the same micro-expression database is usually established under the same experimental paradigm. The training set and test set are selected from the same database, which often cannot effectively identify other micro-expression databases. The samples in the sample, the generalization ability is poor

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