Publication Type:

Conference Paper

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 542, p.337-345 (2018)

ISBN:

9789811032226

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021196620&doi=10.1007%2f978-981-10-3223-3_32&partnerID=40&md5=727dc17b597d349e1c68b6485f0bcce1

Keywords:

Artificial intelligence, Automatic facial expression analysis, Bayesian networks, Classification algorithm, Customer satisfaction, Deep belief networks, Deep learning, Education, Face recognition, Facial expression classification, Facial expression recognition, facial expressions, Hidden Markov models, Human computer interaction, Intelligent computing, Learning algorithms, Learning systems, Machine learning approaches, Machine learning techniques, Markov processes, Support vector machines, Video conferencing

Abstract:

Automatic Facial Expression analysis has enthralled increasing attention in the research community in excess of two decades and its expedient in many application like, face animation, customer satisfaction studies, human-computer interaction and video conferencing. The precisely classifying different emotion is an essential problem in facial expression recognition research. There are several machine learning algorithms applied to facial expression recognition expedition. In this paper, we surveyed three different machine learning algorithms such as Bayesian Network, Hidden Markov Model and Support Vector machine and we attempt to answer following questions: How classification algorithm used its characteristics for emotion recognition? How various parameters in learning algorithm is devoted for better classification? What are the robust features used for training? Finally, we examined how advances in machine learning technique used for facial expression recognition?. © Springer Nature Singapore Pte Ltd. 2018.

Cite this Research Publication

A. Baskar and T. Kumar, G., “Facial expression classification using machine learning approach: A review”, in Advances in Intelligent Systems and Computing, 2018, vol. 542, pp. 337-345.

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