M. Nakano, Y. Mitsukura, M. Fukumi, and N. Akamatsu (Japan)
Smiles recognition, Simple principal component analysis, Neural networks, Recognition of facial expressions
The concern about man-machine interface has increased in recent years, and is expecting application of the recognition of facial expressions. As one of the face expressing method for discriminating front faces, there is a method of com pressing dimensions of the feature vector into low dimen sions by using the principal component analysis (PCA). In this paper, the simple principal component analysis (SPCA) is applied to dimensionality compression of por tions that constitute a face, which is a data-oriented fast method. An angle (cos θ) is calculated using the eigenvec tor and the gray scale image vector of each picture pattern. By using the value of cos θ, similarity between true and false (plastic) smiles is clarified and the true smile is dis criminated. Finally, in order to demonstrate the effective ness of the proposed face smile or false classifying method, computer simulations are done.
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