Abstract
This paper presents a simple yet efficient and completely automatic approach to recognize six fundamental facial expressions using Local Binary Patterns (LBPs) texture features. A system is proposed that can automatically locate four important facial regions from which the uniform LBPs features are extracted and concatenated to form a 236 dimensional enhanced feature vector to be used for six fundamental expressions recognition. The features are trained using three widely used classifiers: Naive bayes, Radial Basis Function Network (RBFN) and three layered Multi-layer Perceptron (MLP3). The notable feature of the proposed method is the use of few preferred regions of the face to extract the LBPs features as opposed to the use of entire face. The experimental results obtained from MMI database show proficiency of the proposed features extraction method.
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Majumder, A., Behera, L., Subramanian, V.K. (2013). Facial Expression Recognition with Regional Features Using Local Binary Patterns. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_67
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DOI: https://doi.org/10.1007/978-3-642-40261-6_67
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