An Overview on Facial Expression Perception Mechanisms

International Journal of Computer Science and Engineering
© 2019 by SSRG - IJCSE Journal
Volume 6 Issue 4
Year of Publication : 2019
Authors : Ankit Jain, Kirti Bhatia, Rohini Sharma, Shalini Bhadola
: 10.14445/23488387/IJCSE-V6I4P105

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Citation:
MLA Style:

Ankit Jain, Kirti Bhatia, Rohini Sharma, Shalini Bhadola, "An Overview on Facial Expression Perception Mechanisms" SSRG International Journal of Computer Science and Engineering 6.4 (2019): 19-24.

APA Style:

Ankit Jain, Kirti Bhatia, Rohini Sharma, Shalini Bhadola,(2019). An Overview on Facial Expression Perception Mechanisms. SSRG International Journal of Computer Science and Engineering 6(4), 19-24.

Abstract:

A lot of information can be perceived through human expressions. We cannot learn the languages of entire world; rather we can interpret most of the expressions of a person in the universe. A facial expression provides information about the condition of user’s conduct in different situations and places. Facial expression can be computerized through various human-computer interface and programming methodologies. The facial expression perception includes detection of face, extraction of features and finally determination of the type of the expression. In this work, we have taken an overview of the numerous facial expression perception mechanisms available in the literature.

References:

[1] M. K. Mandal, R. pandey and A. B. Prasad, ―Facial Expressions of Emotions and Schizophrenia: A Review,‖ Schizophrenia Bulletin, vol. 24, Issue 3, pp. 399– 412, Jan 1998.
[2] A. Butalia, M. Ingle and P. Kulkarni, ―Facial Expression Recognition for Security,‖ International Journal of Modern Engineering Research (IJMER), vol. 2, Issue 4, pp.1449-1453, July 2012.
[3] Y. Wu, W. Liu and J. Wang, ―Application of Emotional Recognition in Intelligent Tutoring System,‖ in Proc. WKDD, 2008, p. 5.
[4] Z. Zhang and J. Zhang, ―A New Real-Time Eye Tracking for Driver Fatigue Detection,‖ in Proc. ITS Telecommunications, 2006, p. 109.
[5] R. Sharma, ―Face recognition using principal component analysis: A survey,‖ in Proc. ICRDCIT, 2018, pp. 59-62.
[6] Sanju, K. Bhatia and R. Sharma, ―An analytical survey on face recognition systems,‖ International Journal of Industrial Electronics and Electrical Engineering, vol. 6, Issue-3,pp. 61-68, Mar 2018.
[7] Sanju, K. Bhatia, R. Sharma, ―Pca and Eigen Face Based Face Recognition Method, Journal of Emerging Technologies and Innovative Research,‖ vol. 5, Issue 6, pp. 491-496. June 2018.
[8] R. Sharma, ―Fisher faces: A Linear Discriminant Analysis Method,‖ in Proc. of ARSSS July, 2018, p. 1-3.
[9] M.J. Lyons, J. Budynek and S. Akamatsu, ―Automatic classification of single facial images,‖ IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, Issue 12, pp. 1357-1362, Dec 1999.
[10] P.N. Belhumeur, J.P. Hespanha and D. Kriegman, ―Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,‖ Pattern Analysis and Machine Intelligence, IEEE Transactions, vol. 19, Issue 7, pp.711-720, July1997.
[11] T. Jabid, M.H. Kabir, O. Chae, ―Facial expression recognition using local directional pattern ,‖ in Proc. ICIP, 2010, p. 1605.
[12] Y. Tong, R. Chen and Y. Cheng, ―Facial expression recognition algorithm using lgc based on horizontal and diagonal prior principle,‖ Optik-International Journal for Light and Electron Optics, vol. 125, Issue 16,pp. 4186-4189, Aug 2014.
[13] T Ojala, M. Pietik ̈ainen, and D. Harwood, ―A comparative study of texture measures with classification based on featured distrib utions,‖ Pattern recognition, vol. 29, Issue 1, pp. 51-59, Jan 1996.
[14] C.-W. Hsu, C.-C. Chang, and C.- J.Lin, ―A Practical Guide to Support Vector Classification,‖ , Department of Computer ScienceNational Taiwan University, Taipei 106, Taiwan, 2003.
[15] N.S. Altman, ―An introduction to kernel and nearest-neighbor nonparametric regression,‖ The American Statistician, vol. 46, Issue. 3, pp. 175-185, Aug 1992.
[16] J. Kumari, R.Rajesh and KM. Pooja, ―Facial expression recognition: A survey,‖ Procedia Computer Science, Vol. 58, pp. 486 – 491, 2015.
[17] A. Shukla and D. D. Dighe, ―A review on automatic facial expression recognition systems, International journal of engineering sciences & research Technology,‖ vol. 5, Issue 4, pp. 272- 279, April 2016.
[18] M. Abdulrahman and A. Eleyan, ―Facial Expression Recognition Using Support Vector Machines Destek Vektör Makineleri ile Yüz İfade Tanıma,‖ in Proc. SIU, 2015, p. 1-4.
[19] H. Khalifa, R.Goebel and I. Cheng, ―Facial expression recognition using SVM classification on mic-micro patterns,‖ in Proc. ICIP, 22 February 2018 , p. 5.
[20] A. Bajpai and K. Chadha, ―Real-time Facial Emotion Detection using Support Vector Machines,‖ International Journal of Advanced Computer Science and Applications, vol. 1, pp. 22-26, 2010.
[21] L. Chen, C. Zhou and L. Shen, ―facial Expression Recognition Based on SVM in E-learning,‖ in Proc. computer supported education , 2012, p. 781-787.
[22] N. Rose, ―Facial Expression Classification using Gabor and Log-Gabor Filters,‖ in Proc. Automatic Face and Gesture Recognition, 2006. Pp.5.
[23] T. Wu, M. S. Bartlett and J. R. Movellan, ―Facial Expression Recognition Using Gabor Motion Energy Filters,‖ in Proc. Computer Society Conference on Computer Vision and Pattern Recognition, 09 August 2010, pp. 1-6.
[24] Y. Tayal, P. Pandey and D. B. V. Singh, "Face Recognition using Eigenface", International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), vol. 3, issue 1, pp. 50-55, Dec.12-Feb.2013.
[25] Y. Buhee and Sukhanlee, ―Robust Face Detection Based on Knowledge-Directed Specification of Bottom-Up Saliency‖, ETRI Journal, vol. 33, issue.4, pp.600-611,August 2011.
[26] C. Shan, S. Gong and P. W. Mcowan, "Facial expression recognition based on Local Binary Patterns: A
comprehensive Study," Image and Vision Computing, vol. 27, pp.803-816, 2009.
[27] L. F. Chen, H. Y. M. Liao, M. T. Ko, J. C. Lin and G. J. Yu, ―A new LDA-based face recognition system which can solve the small sample size problem,‖ Pattern Recognition, vol. 33, pp.1713-1726, 2000.

Key Words:

Face perception system, face detection, expression classification.