Abstract
The following machine learning scheme is commonly used for the recognition of facial expressions: First, the face is detected in the image. Second, tracking techniques are applied, based on active shape models; then, from the tracking of the characteristic points, a description of the facial expression is carried out, using characterization methods based on shape and/or texture; in the case of high dimension vectors, methods of features selection are applied; and finally they are classified in one of the basic expressions. In the latest years, techniques based on sparse representation methods to classify facial expression have been successfully developed. This paper aims at evaluating these methods’ performance from the training of the representation model using K-SVD. A characterization scheme of facial expression is assessed using JAFFE y CK+ databases, with or without the use of the K-SVD method, achieving a value of 0.9755 of accuracy in the classification. The obtained results prove the feasibility in the use of this method in the facial expressions classifiers based on sparse representation.
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Acknowledgements
This work was partially supported by the Projects TIN2012-35427 and TIC2015-67149-c3-2-R with FEDER support, of the Spanish Government, and “XI Convocatoria de Ayudas Para Proyectos de Cooperación Universitaria al Desarrollo – 2014 y 2015 de la UIB”. The authors also thank the Mathematics and Computer Science Department at the University of the Balearic Islands for its support.
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Oliveros, E.R., Coello, G., Marrero-Fernández, P., Buades, J.M., Jaume-i-Capó, A. (2016). Evaluation of K-SVD Method in Facial Expression Recognition Based on Sparse Representation Problems. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2016. Lecture Notes in Computer Science(), vol 9756. Springer, Cham. https://doi.org/10.1007/978-3-319-41778-3_14
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