A Novel Face Recognition Method
AISC '02/Calculemus '02 Proceedings of the Joint International Conferences on Artificial Intelligence, Automated Reasoning, and Symbolic Computation
Deterioration of visual information in face classification using Eigenfaces and Fisherfaces
Machine Vision and Applications
Histograms, wavelets and neural networks applied to image retrieval
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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A expansion encoding approach to image classification is presented. Localized principal components or “eigenpaxels” are used as a set of basis functions to represent images. That is, principal-component analysis is applied locally rather than on the entire image. The “eigenpaxels” are statistically determined using a database of the images of interest. Classification based on visual similarity is achieved through the use of a single-layer error-correcting neural network. Expansion encoding and the technique of subsampling are key elements in the processing stages of the eigenpaxel algorithm. Tested using a database of frontal face images consisting of 40 individuals, the algorithm exhibits equivalent performance to other comparable but more cumbersome methods. In addition, the technique is shown to be robust to various types of image noise