Abstract
This paper proposes a facial expression recognition algorithm, which automatically detects and segments the face regions of interest (ROI) such as the forehead, eyes and mouth, etc. Proposed scheme initially detects the image face and segments it in two regions: forehead/eyes and mouth. Next each of these regions is segmented into N × M blocks which are characterized using 54 Gabor functions that are correlated with each one of the N × M blocks. Next the principal component analysis (PCA) is used for dimensionality reduction. Finally, the resulting feature vectors are inserted in a proposed classifier based on clustering techniques which provides recognition results closed to those provided by the support vector machine (SVM) with much less computational complexity. The experimental results show that proposed system provides a recognition rate of about 98 % when only one ROI is used. This recognition rate increases to about 99 % when the feature vectors of all ROIs are concatenated. This fact allows achieving recognition rates higher than 97 %, even when one of the two ROI are totally occluded.
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Acknowledgements
We thank the National Science and Technology Council of Mexico (CONACYT) and to the Instituto Politecnico Nacional for the financial support during the realization of this research.
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Hernandez-Matamoros, A., Bonarini, A., Escamilla-Hernandez, E., Nakano-Miyatake, M., Perez-Meana, H. (2015). A Facial Expression Recognition with Automatic Segmentation of Face Regions. In: Fujita, H., Guizzi, G. (eds) Intelligent Software Methodologies, Tools and Techniques. SoMeT 2015. Communications in Computer and Information Science, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-319-22689-7_41
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DOI: https://doi.org/10.1007/978-3-319-22689-7_41
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