Micro-expression feature extraction and recognition method based on deep learning

Abstract

The invention provides a micro-expression feature extraction and recognition method based on deep learning, and the method comprises the following steps: constructing a four-layer pyramid optical flow model based on a key frame, inputting a micro-expression video key frame, and obtaining an optical flow feature; extracting LBP (Local Binary Pattern) features from three orthogonal planes of the micro-expression image sequence, and cascading normalized feature gradient histograms obtained in three dimensions into an LBP-TOP (Local Binary Pattern-TOP) histogram vector; the optical flow features are converted into histograms, cascade fusion is carried out on the histograms and LBP-TOP histogram vectors of the image sequences, and histogram representation of fusion features is obtained; constructing a shallow CNN model based on a residual module, introducing a macro expression data set and a micro expression data set, and constructing a cross-data-set micro expression recognition model based on a CNN-GCN transfer learning network; and inputting the fusion features into a trained cross-dataset micro-expression recognition model based on a CNN-GCN transfer learning network for classification to obtain a micro-expression type. According to the method, the micro-expression recognition precision is effectively improved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a method for extracting and recognizing micro-expression features based on deep learning. Background technique [0002] Micro-expression is a kind of spontaneous, relatively subtle facial expression movement signal. Different from macro-expression signals, micro-expressions will not change with subjective will, and the duration is about 1 / 25 to 1 / 5 second, with a small range. Studies have shown that micro-expressions can reflect people's underlying real emotions in a state of depression . Since there is a great correlation between the expression of micro-expression and the psychological state when lying, micro-expression recognition has a good application prospect in the fields of national security, clinical medicine, case detection, judicial interrogation and so on. [0003] In terms of micro-expression feature extraction, the existing optical flow method is mainly a...

AI Technical Summary

Problems solved by technology

However, due to factors such as the insufficient number of samples in the micro-expression database, the above-mentioned model has an over-fitting problem, and the accuracy of the model is low

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