Recently, an eigenface method by using the principal component analysis (PCA) is popular in a filed of facial expressions recognition. In this study, in order to achieve high-speed PCA, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. By using Neural Networks (NN), the difference in value of cos θ between true and false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smile, computer simulations are done with real images.
CITATION STYLE
Nakano, M., Mitsukura, Y., Fukumi, M., Akamatsu, N., & Yasukata, F. (2003). True smile recognition using neural networks and simple PCA. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2773 PART 1, pp. 631–637). Springer Verlag. https://doi.org/10.1007/978-3-540-45224-9_86
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