Abstract:
One of the most important modules of any bio-metric system is the feature extraction module. Given a sample it is important for the feature extraction method to extract a...Show MoreMetadata
Abstract:
One of the most important modules of any bio-metric system is the feature extraction module. Given a sample it is important for the feature extraction method to extract a rich set of features that can be used for identity recognition. This form of feature extraction has been referred to as Type I feature extraction and for some biometric systems it 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 (also known as feature selection). In this paper, we compare two GEC-based Type II feature extraction methods as applied to periocular-based recognition, an exciting new area of research within the Biometric research community that to date has used Type I feature extraction exclusively. Our results show that GEC-based Type II feature extraction is effective in optimizing recognition accuracy as well as minimizing the overall feature set size.
Published in: IEEE Congress on Evolutionary Computation
Date of Conference: 18-23 July 2010
Date Added to IEEE Xplore: 27 September 2010
ISBN Information: