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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/17169

Title: Color Image Based Face Recognition
Authors: Ganapathi, Tejaswini
Advisor: Plataniotis, Konstantinos N.
Department: Electrical and Computer Engineering
Keywords: Color Face Recognition
Small Sample Size Problem
Issue Date: 24-Feb-2009
Abstract: Traditional appearance based face recognition (FR) systems use gray scale images, however recently attention has been drawn to the use of color images. Color inputs have a larger dimensionality, which increases the computational cost, and makes the small sample size (SSS) problem in supervised FR systems more challenging. It is therefore important to determine the scenarios in which usage of color information helps the FR system. In this thesis, it was found that inclusion of chromatic information in FR systems is shown to be particularly advantageous in poor illumination conditions. In supervised systems, a color input of optimal dimensionality would improve the FR performance under SSS conditions. A fusion of decisions from individual spectral planes also helps in the SSS scenario. Finally, chromatic information is integrated into a supervised ensemble learner to address pose and illumination variations. This framework significantly boosts FR performance under a range of learning scenarios.
URI: http://hdl.handle.net/1807/17169
Appears in Collections:Master
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering - Master theses

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