Abstract
In this paper, a coarse-to-fine classification scheme is used to recognize facial expressions (angry, disgust, fear, happiness, neutral, sadness and surprise) of novel expressers from static images. In the coarse stage, the seven-class problem is reduced to a two-class one as follows: First, seven model vectors are produced, corresponding to the seven basic facial expressions. Then, distances from each model vector to the feature vector of a testing sample are calculated. Finally, two of the seven basic expression classes are selected as the testing sample’s expression candidates (candidate pair). In the fine classification stage, a K-nearest neighbor classifier fulfils final classification. Experimental results on the JAFFE database demonstrate an average recognition rate of 77% for novel expressers, which outperforms the reported results on the same database.
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Feng, X., Hadid, A., Pietikäinen, M. (2004). A Coarse-to-Fine Classification Scheme for Facial Expression Recognition. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_81
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DOI: https://doi.org/10.1007/978-3-540-30126-4_81
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