Recognition of Smiling Faces Using Neural Networks and Spca

Abstract

Research on "man-machine interface" has increased in many fields of engineering and its application to facial expressions recognition is expected. The eigenface method by using the principal component analysis (PCA) is popular in this research field. However, it is not easy to compute eigenvectors with a large matrix if the cost of calculation when applying it for time-varying processing is taken into consideration. In this paper, 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. A value of cos θ is calculated using an eigenvector by SPCA as well as a gray-scale image vector of each picture pattern. By using neural networks (NNs), the difference in the value of cos θ between the true and the 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 smiles, computer simulations are done with real images. Furthermore, an experiment using the self-organisation map (SOM) is also conducted as a comparison.

Feature Generation Method by Geometrical Interpretation of Fisher Linear Discriminant Analysis

“…As for this Simple-PCA, its effectiveness has been shown in many applications. In the field of face information processing, the use of it as feature extraction is more than the use as dimension compression (8) (9) . However, the distribution between classes is not considered at all because the Simple-PCA is feature generation based on the principal component vector.…”