The purpose of this paper is to improve the accuracy of the facial expression recognition by using genetic algorithm (GA) with an appropriate fitness evaluation function and Pareto optimization model with two new objective functions.
To achieve facial expression recognition with high accuracy, the Haar-like features representation approach and the bilateral filter are first used to preprocess the facial image. Second, the uniform local Gabor binary patterns are used to extract the facial feature so as to reduce the feature dimension. Third, an improved GA and Pareto optimization approach are used to select the optimal significant features. Fourth, the random forest classifier is chosen to achieve the feature classification. Subsequently, some comparative experiments are implemented. Finally, the conclusion is drawn and some future research topics are pointed out.
The experiment results show that the proposed facial expression recognition algorithm outperforms ones in the existing literature in terms of both the actuary and computational time.
The GA and Pareto optimization algorithm are combined to select the optimal significant feature. To improve the accuracy of the facial expression recognition, the GA is improved by adjusting an appropriate fitness evaluation function, and a new Pareto optimization model is proposed that contains two objective functions indicating the achievements in minimizing within-class variations and in maximizing between-class variations.
This work was supported in part by the National Natural Science Foundation of China under Grants 61473076 and 61329301, the Shu Guang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 13SG34, the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, the Program for the Fundamental Research of the Shanghai Committee of Science and Technology under Grant 15JC1400600, the Fundamental Research Funds for the Central Universities under Grant EG2016019 and the DHU Distinguished Young Professor Program.
Wang, F., Shen, B., Sun, S. and Wang, Z. (2016), "Improved GA and Pareto optimization-based facial expression recognition", Assembly Automation, Vol. 36 No. 2, pp. 192-199. https://doi.org/10.1108/AA-11-2015-110Download as .RIS
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