Discriminating angry, happy and neutral facial expression: a comparison of computational models
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AbstractRecognizing expressions are a key part of human social interaction, and processing of facial expression information is largely automatic for humans, but it is a non-trivial task for a computational system. The purpose of this work is to develop computational models capable of differentiating between a range of human facial expressions. Raw face images are examples of high dimensional data, so here we use two dimensionality reduction techniques: Principal Component Analysis and Curvilinear Component Analysis. We also preprocess the images with a bank of Gabor filters, so that important features in the face images are identified. Subsequently the faces are classified using a Support Vector Machine. We show that it is possible to differentiate faces with a neutral expression from those with a happy expression and neutral expression from those of angry expressions and neutral expression with better accuracy. Moreover we can achieve this with data that has been massively reduced in size: in the best case the original images are reduced to just 5 components with happy faces and 5 components with angry faces.
CitationShenoy, A., Anthony, S., Frank, R.J. and Davey, N. (2009) 'Discriminating Angry, Happy and Neutral Facial Expression: A Comparison of Computational Models', in Palmer-Brown, D.; Draganova, C.; Pimenidis, E.; Mouratidis, H. (Eds.) Engineering Applications of Neural Networks - Proceedings 11th International Conference, EANN 2009, series Communications in Computer and Information Science, 43: 200-209
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