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What Happens in Face During a Facial Expression? Using Data Mining Techniques to Analyze Facial Expression Motion Vectors

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Abstract

Automatic facial expression recognition is a big challenge in human–computer interaction. Analyzing the changes in the face during a facial expression can be used for this purpose. In this paper, these changes are extracted as a number of motion vectors. These motion vectors are extracted using an optical flow algorithm. Then, they are used to analyze facial expressions by some of the data mining algorithms. This analysis has not only determined what changes occur in the face during facial expression but has also been used to recognize facial expressions. Cohen-Kanade facial expression dataset was used in this research. Based on our findings, the vertical lengths of motion vectors created in the lower part of the face have the greatest impact on the classification of facial expressions. Among the investigated classification algorithms, deep learning, support vector machine, and C5.0 had better performance, yielding an accuracy of 95.3%, 92.8%, and 90.2% respectively.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon request.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, 74617-81189, Iran

    Mohamad Roshanzamir

  2. Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain

    Mahboobeh Jafari & Afshin Shoeibi

  3. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia

    Roohallah Alizadehsani & Abbas Khosravi

  4. Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Mahdi Roshanzamir

  5. Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain

    Juan M. Gorriz

  6. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Juan M. Gorriz

  7. Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Saeid Nahavandi

  8. Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, 02134, USA

    Saeid Nahavandi

  9. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia

    U. Rajendra Acharya

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Contributions

Conceptualization, Mo.R., R.A., M.J. and Ma.R.; methodology, A.S., J.M.G. and A.K.; software, Mo.R., R.A., M.J. and Ma.R.; validation, A.S., J.M.G. and A.K.; formal analysis, Mo.R., R.A. and Ma.R.; investigation, S.N. and U.R.A.; resources, A.S., J.M.G. and A.K.; data curation, Mo.R., R.A., M.J. and Ma.R.; writing—original draft preparation, Mo.R., R.A., M.J. and Ma.R.; visualization, J.M.G. and A.K.; supervision, S.N. and U.R.A.

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Correspondence to Mahboobeh Jafari.

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Roshanzamir, M., Jafari, M., Alizadehsani, R. et al. What Happens in Face During a Facial Expression? Using Data Mining Techniques to Analyze Facial Expression Motion Vectors. Inf Syst Front (2024). https://doi.org/10.1007/s10796-023-10466-7

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