Open AccessA FACIAL EXPRESSION RECOGNITION METHOD USING LOCAL NONLINEAR FEATURES
Author(s) -
Pengcheng Wei,
Bo Wang,
Mohanad Ahmed Almalki,
Xiaojun Dai,
Xianghua Zhang
Publication year - 2022
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x22401053
Subject(s) - pattern recognition (psychology) , artificial intelligence , computer science , facial recognition system , face (sociological concept) , feature (linguistics) , face hallucination , facial expression , three dimensional face recognition , support vector machine , preprocessor , feature vector , expression (computer science) , face detection , social science , linguistics , philosophy , sociology , programming language
The purposes are to recognize the facial expression information and establish a fast and accurate facial expression recognition algorithm. First, the noise caused by face image shooting is eliminated by analyzing the primary methods of face recognition and preprocessing the images. Then, the Nonlinear Support Vector Machine (NSVM) is combined with the decision tree to obtain the Nonlinear Support Vector Machine Decision Tree (NSMMD) algorithm. By introducing the binary tree, NSVM has the ability of multi-classification and can extract the feature information of the face image to obtain the characteristic face. Then, the algorithm flow is introduced. Second, the characteristic face data are recognized and classified based on Generalized Regression Neural Network (GRNN), and the expression data contained in the face images are obtained to recognize the expressions of the face image. Finally, the designed face recognition algorithm and facial expression recognition algorithm are tested. The results show that the designed NSMMD algorithm has a weak dependence on feature dimension, and the face recognition rate for different datasets is above 80.1%. The GRNN algorithm will be affected by smooth parameters, its optimal parameter value is 0.1, and its recognition rate of expressions on various databases is above 92%. Therefore, the designed face recognition algorithm and facial expression recognition algorithm can provide sound recognition effects, which can reference the study of facial image feature recognition.