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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/18902

Title: Linear Feature Extraction with Emphasis on Face Recognition
Authors: Mahanta, Mohammad Shahin
Advisor: Plataniotis, Konstantinos N.
Department: Electrical and Computer Engineering
Keywords: Feature Extraction
Face Recognition
Sufficient Statistic
Issue Date: 15-Feb-2010
Abstract: Feature extraction is an important step in the classification of high-dimensional data such as face images. Furthermore, linear feature extractors are more prevalent due to computational efficiency and preservation of the Gaussianity. This research proposes a simple and fast linear feature extractor approximating the sufficient statistic for Gaussian distributions. This method preserves the discriminatory information in both first and second moments of the data and yields the linear discriminant analysis as a special case. Additionally, an accurate upper bound on the error probability of a plug-in classifier can be used to approximate the number of features minimizing the error probability. Therefore, tighter error bounds are derived in this work based on the Bayes error or the classification error on the trained distributions. These bounds can also be used for performance guarantee and to determine the required number of training samples to guarantee approaching the Bayes classifier performance.
URI: http://hdl.handle.net/1807/18902
Appears in Collections:Master
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering - Master theses

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