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A Study on Efficient Facial Expression Recognition System for Customer Satisfaction Feedback  

Kang, Min-Sik (남서울대학교 산업경영공학과)
Publication Information
Convergence Security Journal / v.12, no.4, 2012 , pp. 41-47 More about this Journal
Abstract
For competitiveness of national B2C (Business to Customer) service industry, improvement of process and analysis focused on customer and change of service system are needed. In other words, a business and an organization should deduce and provide what kind of services customers want. Then, evaluate customers' satisfaction and improve the service quality. To achieve this goal, accurate feedbacks from customers play an important role; however, there are not quantitative and standard systems a lot in nation. Recently, the researches about ICT (Information and Communication Technology) that can recognize emotion of human being are on the increase. The facial expression recognition among them is known as most efficient and natural human interface. This research analyzes about more efficient facial expression recognition and suggests a customer satisfaction feedback system using that.
Keywords
B2C(Business to Customer); Customer Satisfaction Feedback; ICT(Information and Communication Technology); Facial Expression Recognition;
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  • Reference
1 P. Ekman, W. Frisen, "Facial Action Coding System : A Technique for the Measurement of Facial Movement", Consulting Psychologists Press,1978.
2 Kim, T. Chang,A. & Pantland, A."Meeting Mediator: Enhancing Group Collaboration with Sociometric Feedback ", Proceedings of Conference on Computer Supported Collaborative Work, 457-466, 2008.
3 J. Lien, T. Kanade, C. Li, "Detection, tracking, and classification of action units in facial expression", J. of Robotics and Autonomous Systems, Vol. 31, No. 3, pp. 131-146, 2000.   DOI   ScienceOn
4 M. Turk, A. Pentland, "Eigenfaces for recognition", J. of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991.   DOI   ScienceOn
5 P. Penev, J. Atick, "Local feature analysis: a general statistical theory for object representation", Network : Computation in Neural Systems, Vol. 7,pp. 477-500, 1996.   DOI   ScienceOn
6 P. Belhumeur, J. Hespanha, D. Kriegman,"Eigenfaces vs. fisherfaces: Recognition using class specific linear projection", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, 1997.   DOI   ScienceOn
7 C. Padgett, G. Cottrell, "Representing face images for emotion classification", Advances in Neural Information Processing Systems, Vol. 9, MIT Press. 1997.
8 A. Bell, T. Sejnowski, "An information maximization approach to blind separation and blind deconvolution", Neural Computation, Vol. 7, pp. 1129-1159, 1995.   DOI   ScienceOn
9 M.Bartlett. "Face Image Analysis by Unsupervised Learning and Redundancy Reduction ",PhD thesis, Univ. of California ,1998.
10 M.Pantic ,"An Expert System for Multiple Emotional Classification of Facial Expressions" Proc. 11th IEEE Int. Conf. on Tools with Artificial Intelligence, 113-120,1999.
11 이오영., 박혜영., 최승진., "Factorial code 표현법을 이용한 얼굴인식", 한국통신학회논문지 Vol. 26, No. 10B, pp. 1444-1452, 2001.
12 한수정 외,"ICA-factorial 표현법을 이용한 얼굴감정인식", 퍼지 및 지능시스템학회 논문지 Vol.13 No.3 pp.371-376 ,2003.