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Facial-expression recognition based on a low-dimensional temporal feature space

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Abstract

This paper suggests a facial-expression recognition in accordance with face video sequences based on a newly low-dimensional feature space proposed. Indeed, we extract a Pyramid of uniform Temporal Local Binary Pattern representation, using only XT and YT orthogonal planes (PTLBPu2). Then, a Wrapper method is applied to select the most discriminating sub-regions, and therefore, reduce the feature space that is going to be projected on a low-dimensional feature space by applying the Principal Component Analysis (PCA). Support Vector Machine (SVM) and C4.5 algorithm have been tested for the classification of facial expressions. Experiments conducted on CK + and MMI, which are the two famous facial-expression databases, have shown the effectiveness of the approach proposed under a lab-controlled environment with more than 97% of recognition rate as well as under an uncontrolled environment with more than 92%.

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Acknowledgements

The authors are grateful to Sofiene HADDED, teacher of English at the Faculty of Economics and Management of Sfax, Tunisia for having proofread the manuscript.

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Authors and Affiliations

  1. MIR@CL Laboratory, Faculty of Economics and Management of Sfax (FSEGS), University of Sfax, Sfax, Tunisia

    Taoufik Ben Abdallah

  2. Saudi Electronic University, Riyadh, Kingdom of Saudi Arabia

    Radhouane Guermazi

  3. MIR@CL Laboratory, Faculty of Sciences of Sfax (FSS), University of Sfax, Sfax, Tunisia

    Mohamed Hammami

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  1. Taoufik Ben Abdallah
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  2. Radhouane Guermazi
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  3. Mohamed Hammami
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Correspondence to Taoufik Ben Abdallah.

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Abdallah, T., Guermazi, R. & Hammami, M. Facial-expression recognition based on a low-dimensional temporal feature space. Multimed Tools Appl 77, 19455–19479 (2018). https://doi.org/10.1007/s11042-017-5354-x

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  • DOI: https://doi.org/10.1007/s11042-017-5354-x

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