Модели, системы, сети в экономике, технике, природе и обществе (May 2023)
FACIAL MICRO-EXPRESSION RECOGNITION USING CLASSIFIERS BASED ON MACHINE LEARNING METHODS
Abstract
Background. The problem of the automatic facial micro-expression recognition from the image sequence can be solved using technologies on the basis of computer vision methods and algorithms. At present, investigations of such technologies are carried out. However, the accuracy of the recognition results depends essentially on the selection of methods, algorithms, and also their parameters at each stage of the used technology. The correct facial micro-expression recognition is in turn the key factor to solve the problem of the recognition of the hidden emotions experienced by a human. Facial micro-expressions are generated on the basis of the combination of facial micro-movements. The research objective is the investigation of the facial micro-movement detection accuracy dependence on the selection of the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) feature descriptor algorithm parameters and machine learning method for the classification of the feature vectors. Materials and methods. The Spontaneous Actions and Micro-Movements (SAMM) dataset is used as the initial data. The study was made of changes in the accuracy of detection of facial micromovements for classifiers based on the SVM and multilayer perceptron MLP when changing the parameters of the LBP-TOP algorithm. Results. As a result of the study, it is ascertained that the best result for the SVM classifier is the 94 % detection accuracy, and the best detection accuracy for the MLP classifier is 98 %. Thus, because of optimal selection of algorithm parameters both classifiers could handle the problem of the facial micro-movement detection. Conclusions. The both considered methods MLP and SVM show acceptable results to solve the problem of the facial micro-expression recognition with a slight advantage of MLP in comparison with SVM.
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