Abstract
Facial expression recognition is a well discussed problem. Several machine learning methods are used in this regard. Among them, Adaboost is popular for its simplicity and considerable accuracy. In Adaboost, decisions are made based on the weighted majority vote of several weak classifiers. However, such weighted combination may not give expected accuracy due to the lack of proper uncertainty management. In this paper, we propose to adopt the Dempster Shafer theory (DST) of Evidence based solution where mass values are calculated from k-nearest neighboring feature information based on some distance metric, and combined together using DST. Experiments on a renowned dataset demonstrate the effectiveness of the proposed method.
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Shoyaib, M., Abdullah-Al-Wadud, M., Zahid Ishraque, S.M., Chae, O. (2012). Facial Expression Classification Based on Dempster-Shafer Theory of Evidence. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_25
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DOI: https://doi.org/10.1007/978-3-642-29461-7_25
Publisher Name: Springer, Berlin, Heidelberg
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