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  •   Abozar Atya Mohamed Atya

  •   Khalid Hamid Bilal

Abstract

The advent of artificial intelligence technology has reduced the gap between humans and machines as equips man to create more near-perfect humanoids. Facial expression is an important tool to communicate one’s emotions as a non-verbally overview of emotion recognition using facial expressions. A remarkable advantage of such a technique recently improved public security through tracking and recognizing, thus led to the high attention to keep up the scientific research in the field. The approaches used for facial expression include classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolution Neural Network (CNN), Active Appearance and Machine learning which all used to classify emotions based on certain parts of interest on the face like lips, lower jaw, eyebrows, cheeks and many more. By comparison, the reviews have shown that the average accuracy of the basic emotion ranged from 51% up to 100%, whereas carrying through 7% to 13% in the compound emotions, hence indicated that the indispensable emotion is much comfortable to recognize.

Keywords: face recognition; support vector machine; artificial neural network; convolution neural network; machine learning

References

F. De la Torre and J. F. Cohn, "Facial Expression Analysis”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vis. Anal. H, 2016).

Y.-L. Tian, T. Kanade, and J. F. Cohn, "Recognizing action units for facial expression analysis", Proc. IEEE Conf. Compute. Vis. Pattern Recognition. CVPR 2000 Cat NoPR00662, vol. 1, no. 2, pp. 1–19, 2001.

W. Gu, C. Xiang, Y. V. Venkatesh, D. Huang, and H. Lin,"Facial Expression Recognition Using Radial Encoding of Local Gabor Features and Classier Synthesis,'' Pattern Recognit., vol. 45, no. 1, pp. 80_91, 2012.

Y. Rahulamathavan, R. C.-W. Phan, J. A. Chambers, and D. J. Parish," Facial expression recognition in the encrypted domain based on local Sher discriminant analysis, '' IEEE Trans. Affect. Compute.” vol. 4, no. 1, pp. 83_92, Jan./Mar. 2013.

Izard, C.E. " Human Emotions”, Springer, New York (2013) um. pp. 377– 410, 2011.

B. Moghaddam, T. Jebara and A. Pentlandvol, Baysian face recognition, in Pattern Recognition ,Nov 2000.

Advanced Applications for (ANN), Edited by Adel EL-Shahat, Georgia Southern University, Published February 28th 2018.

Deep Convolutional Neural Networks for Image Classifcation Acomprehensive Review,Waseem Rawat and Zenghui Wang , Published in Neural Computation June 2017.

Farshad Ghahramani,Face Recognition:An Engineering Approach,San Jose State Universty 2015.

Alex Smola and S.V.N. Vishwanathan, Introduction to Machine Learning,Published by the press syndicate of the university of cambridge, 2008.

P. Ekman, ``Are there basic emotions'', Psychol. Rev., vol. 99, no. 3, pp. 550_553, 1992.

C. A. Corneanu, M. Oliu, J. F. Cohn, and S. Escalera,"Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications,'' IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 8, pp. 1548_1568, Aug. 2016.

D Y Liliana," Emotion Recognition from Facial Expression using DeepConvolutional Neural Network", International Conference of Computer and Informatics Engineering (IC2IE), Published 2018.

Hai Duong Nguyen , Hyung Jeong Yang , Sonja Yeom and others, Facial Emotion Recognition Using an Ensemble of Multi-Level Convolutional Neural Networks, International Journal of Pattern Recognition and Artificial Intelligence, Published March 2019.

Veena Mayya, Radhika M.Pai, and Man Dhara Pai M.M, "Automatic Facial Expression Recognition Using DCNN”, International Conference On Advances In Computing & Communications (ICACC), Published September 2016.

Ashlesha Vaidya, Hitee Sachdeva and R.Brindha, Facial Expression Recognition Using Convolutional Neural Networks, International Journal of Pure and Applied Mathematics, Published November 2018.

Dinh Viet sang, Nguyen Van Dat and DO Phan Thuan," Facial Expression Recognition Using Deep Convolutional Neural Networks", , Published October 2017.

Jianzhu Guo, Zhenlei and Jun Wan, "Dominant and Complementary Emotion Recognition from Still Images of Faces", IEEE Special Section on Visual Surveillance and Biometrics, publication April 30, 2018.

Awais Mahmood, Sharig Hussain, Khalid Iqbal and Wail S. Elkilani, " Recognition of Facial Expressions under Varying Conditions Using Dual-Feature Fusion", Hindawi Mathematical Problems in Engineering, Published 21 August 2019.

Hongli Zhang, Alireza Joifaei and Mamoun Alazab," A Face Emotion Recognition Method Using Convolutional Neural Network and Image Edge Computing” , IEEE Access, Published October 2019.

Chintan B. Thacker , Ramji M. Makwana, "Human Behaviour Analysis through Facial Expression Recognition in Images using Deep Learning " , International Journal of Innovative Technology and Exploring Engineering (IJITEE), Published December 2019.

Sumalakshmi C.H, P.Vasuki,"Performance Improving of ANN with Pre-processing Stage in Human Face Expression Recognition System", International Journal of Innovative Technology and Exploring Engineering (IJITEE), Published February 2020.

Md. Forhad Ali, Mehenag Khatun and Nakib Aman Turzo," Facial Emotion Detection Using Neural Network” , International Journal of Scientific & Engineering Research, Published August-2020.

Kiavash Bahreini, Wim Van der Vegt and Wim Westera, ," A fuzzy logic approach to reliable real-timerecognition of facial emotions",Multimedia Tools and Applications (2019) 78:18943–18966, Published online: 6 February 2019.

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How to Cite
[1]
Atya, A.A.M. and Bilal, K.H. 2021. Review on Emotion Recognition Using Facial Expressions. European Journal of Electrical Engineering and Computer Science. 5, 3 (May 2021), 1-4. DOI:https://doi.org/10.24018/ejece.2021.5.3.322.