A microexpression recognition method based on sparse projection learning

Abstract

The invention discloses a micro expression recognition method based on sparse projection learning. The method comprises the steps of: Step 1, collecting a micro expression sample, extracting LBP features P, Q, R of three orthogonal planes of the micro expression, and defining C, D, E as feature optimization variables of three orthogonal planes of XY, XT, YT respectively; constructing an optimization model; 2, setting the initial value and maximum value of iterative counting variable t and n and initializing the regularization parameter kappa, kappa max, and the scale parameter ruho; step 3, initializing the expression (shown in the description), calculating C, and updating T1 and Kappa; If the expression (shown in the description) converges or n> nmax, proceeding to step 4; step 4, initializing the expression (shown in the description), calculating D, and updating T2 and Kappa; and if the expression (shown in the description) converges or n>nmax, proceeding to step 5; step 5, initializing the expression (shown in the description), calculating E, and updating T3 and Kappa; and if the expression (shown in the description) converges or n> nmax, proceeding to step 6; step 6, makingt=t+1, if t <= tmax, returning to step 3, otherwise, outputting C, D, E; step 7, optimizing the LBP features of the three orthogonal plane by optimizing the variables C, D and E to obtain a new fusionfeature Ftest, and predicting the emotion category of the test sample through the trained SVM classifier for the fusion feature Ftest.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a micro-expression recognition method for LBP-TOP (Local Binary Patterns from Three Orthogonal Planes) feature optimization, specifically a micro-expression recognition method based on sparse projection learning. Background technique [0002] In recent years, micro-expression recognition has been very active in the field of computer pattern recognition. Unlike normal expressions, micro-expressions are very short-lived, so most people tend to ignore their existence. At present, many teams at home and abroad are actively carrying out research on micro-expression recognition, and have achieved certain results. From the latest research results at home and abroad at this stage, it is found that the currently known methods still have their own limitations. Both data collection, database establishment, feature extraction and theoretical analysis need to be further improved and develop...

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Problems solved by technology

For example, in the literature: Zhao G, Pietikainen M. Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2007, 29(6):915-928, extract three orthogonal The features of the plane represent micro-expressions, but it does not objectively consider the feature fusion problem in the spatial and temporal domains

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