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2015 | OriginalPaper | Buchkapitel

13. Improving the Recognition Performance of Moment Features by Selection

verfasst von : George A. Papakostas

Erschienen in: Feature Selection for Data and Pattern Recognition

Verlag: Springer Berlin Heidelberg

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Abstract

This chapter deals with the selection of the most appropriate moment features used to recognize known patterns. This chapter aims to highlight the need for selection of moment features subject to their descriptive capabilities. For this purpose, some popular moment families are presented and their properties, making them suitable for pattern recognition tasks, are discussed. Two different types of feature selection algorithms, a simple Genetic Algorithm (GA) and the Relief algorithm are applied to select the moment features that better discriminate human faces and facial expressions, under several pose and illumination conditions. Appropriate experiments using four benchmark datasets have been conducted in order to investigate the theoretical assertions. An extensive experimental analysis has shown that the recognition performance of the moment features can be significantly improved by selecting them from a predefined pool, relative to a specific application.

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Titel: Improving the Recognition Performance of Moment Features by Selection
verfasst von: George A. Papakostas
Verlag: Springer Berlin Heidelberg
Buch: Feature Selection for Data and Pattern Recognition
Print ISBN: 978-3-662-45619-4

Electronic ISBN: 978-3-662-45620-0

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-662-45620-0_13

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