Genetic-Based Type II Feature Extraction for Periocular Biometric Recognition: Less is More

Authors:
Joshua Adams;Damon L. Woodard;Gerry Dozier;Philip Miller;Kelvin Bryant;George Glenn
Affiliations:
-;-;-;-;-;-
Venue:
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Year:
2010

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Abstract

Given an image from a biometric sensor, it is important for the feature extraction module to extract an original set of features that can be used for identity recognition. This form of feature extraction has been referred to as Type I feature extraction. For some biometric systems, Type I feature extraction is used exclusively. However, a second form of feature extraction does exist and is concerned with optimizing/minimizing the original feature set given by a Type I feature extraction method. This second form of feature extraction has been referred to as Type II feature extraction (feature selection). In this paper, we present a genetic-based Type II feature extraction system, referred to as GEFE (Genetic & Evolutionary Feature Extraction), for optimizing the feature sets returned by Loocal Binary Pattern Type I feature extraction for periocular biometric recognition. Our results show that not only does GEFE dramatically reduce the number of features needed but the evolved features sets also have higher recognition rates.