Abstract
We propose an efficient algorithm for recognizing facial expressions using biologically plausible features: contours of face and its components with radial encoding strategy. A self-organizing network (SON) is applied to check the homogeneity of the encoded contours and then different classifiers, such as SON, multi-layer perceptron and K-nearest neighbor, are used for recognizing expressions from contours. Experimental results show that the recognition accuracy of our algorithm is comparable to that of other algorithms in the literature on the Japanese female facial expression database. We also apply our algorithm to Taiwanese facial expression image database to demonstrate its efficiency in recognizing facial expressions.
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Acknowledgments
The research reported here was supported by NUS Academic Research Fund R-263-000-362-112. The authors thank Professor Teuvo Kohonen (Helsinki University of Technology) for permission to use his SOM Toolbox. The authors also gratefully acknowledge the anonymous reviewers and editors for their helpful comments to substantially improve the quality of this paper.
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Gu, W.F., Venkatesh, Y.V. & Xiang, C. A novel application of self-organizing network for facial expression recognition from radial encoded contours. Soft Comput 14, 113–122 (2010). https://doi.org/10.1007/s00500-009-0441-1
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DOI: https://doi.org/10.1007/s00500-009-0441-1