A FACIAL EXPRESSION RECOGNITION METHOD USING LOCAL NONLINEAR FEATURES

Pengcheng Wei, Bo Wang, Mohanad Ahmed Almalki (), Xiaojun Dai and Xianghua Zhang
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Pengcheng Wei: School of Mathematics and Information Engineering, Chongqing University of Education, Chongqing, P. R. China
Bo Wang: ��School of Automation, Chongqing, University of Posts and Telecommunications, Chongqing, P. R. China
Mohanad Ahmed Almalki: ��Department of Computer Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Xiaojun Dai: ��School of Automation, Chongqing, University of Posts and Telecommunications, Chongqing, P. R. China
Xianghua Zhang: School of Mathematics and Information Engineering, Chongqing University of Education, Chongqing, P. R. China

FRACTALS (fractals), 2022, vol. 30, issue 02, 1-11

Abstract: 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.

Keywords: Face Detection; Facial Expression Recognition; Nonlinear Data; Bilinear Interpolation; Nonlinear Support Vector Machine; Generalized Regression Neural Network (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22401053

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