An Integrated System for Automatic Face Recognition
This paper presents an Automated Face Recognition (AFR) system capable of providing satisfactory results even with only one training image per individual. To obtain this result an innovative architecture has been devised with the ability to integrate organically new solutions with well-established, even classic, techniques, i.e., Principal Component Analysis (PCA) and Discrete Cosine Transforms (DCT). The process of identification thereby concludes successfully even under trying circumstances; that is, even in the presence of consistent variations in the orientation, scale and expression of the face under observation. Radial Basis Function (RBF) neural networks are used as classifiers, the output of which converge into a single block that in turn adopts a decisional strategy. Experimental results on the Face Recognition Technology (FERET) database demonstrate the validity of our approach, and invite comparison with other systems of face recognition. ...
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