A Method of Improving Accuracy in Expression Recognition
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In order to improve the accuracy of a special kind of facial expression recognition problem, a method for precise face detection and segmentation combined with the particle swarm optimization is proposed. The method uses three key technologies: skin color segmentation, particle swarm search and curve approximation. Firstly, the face contour is roughly obtained through skin color segmentation. Secondly, the accurate face position is detected by particle swarm optimization. Thirdly, the face contour is reduced and regulated further via the curve approximation. The experimental results show that this method can eliminate the interference factor, and then improve the accuracy of expression recognition.
References
Tian YL, Takeo K, Jeffrey FC. Facial Expression Recognition. Handbook of Face Recognition, 2011, Part 2, 487–519.
Huang X., et al. Towards a dynamic expression recognition system under facial occlusion. Pattern Recognition Letters, 2012 ;33(16):2181–2191.
Sariyanidi E., et al. Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 ;37(6):1113–1133.
Xiao R, Zhao QJ, Zhang D, et al. Facial expression recognition on multiple manifolds. Pattern Recognition, 2011;44(1):107–116.
Li S., Deng W. Deep Facial Expression Recognition: A Survey. IEEE Transactions on Affective Computing, 2020:1–25.
Gunes H., et al. Emotion representation, analysis and synthesis in continuous space: A survey. IEEE International Conference on Automatic Face & Gesture Recognition and Workshops, 2011:827–834.
Sarris N, Grammalidis N, Strintzis M.G. FAP extraction using three-dimensional motion estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2002;12(10): 865–876.
Lavagetto F, Pockaj R. An efficient use of MPEG-4 FAP interpolation for facial animation at 70 bits/frame. IEEE Transactions on Circuits and Systems for Video Technology, 2001;11(10):1085–1097.
Matthew Turk, Alex Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991;3(1):71–86.
Bartlett M.S., Movellan J.R., Sejnowski T.J. Face recognition by independent component analysis. IEEE Transactions on Neural Networks, 2002;13(6):1450–1464.
Tanveer Md. I. Eigenface based Facial Expression Classification. ttp://www.mathworks.com/matlabcentral/fileexchange/.
Gonzalez RC, Woods RE, Eddins SL. Digital Image Processing Using MATLAB. Publishing House of Electronics Industry, 2012.
Hsu R. L., Mottaleb M. A., Jain A. K. Face Detection in Color Images. IEEE Transactions on Pattern and Machine Intelligence, 2002;24(5):210–211.
Kennedy J., Eberhart R.C. Particle Swarm Optimization. Proceedings IEEE International Conference on Neural Networks, 1995, pp. 1942–1948.
Eberhart R.C., Shi Y. Particle swarm optimization: developments, applications and resources. Proceedings of the IEEE Congress on Evolutionary Computation, 2001, pp. 81–86.
Zhang J. Experimental Parameter Investigations on Particle Swarm Optimization Acceleration Coefficients. International Journal of Advancements in Computing Technology, 2012;4(5):99–105.
Wen H, Guo CH. Face Recognition with Features Extraction Based on Particle Swarm Optimization. Journal of Xi’an Jiaotong University, 2010;44(4):48–51.
Li ZJ. Location and segmentation of facial features combining PSO algorithm and skin color. International Journal of Science And Engineering, 2019;5(9):32–38.
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