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Improving the Recognition Performance of Moment Features by Selection

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Book cover Feature Selection for Data and Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 584))

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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Authors and Affiliations

  1. Human Machines Interaction (HMI) Laboratory, Department of Computer and Informatics Engineering, Eastern Macedonia and Thrace (EMT) Institute of Technology, Ag. Loukas, 65404, Kavala, Greece

    George A. Papakostas

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  1. George A. Papakostas
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Correspondence to George A. Papakostas .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Urszula Stańczyk

  2. Mawson Lakes Campus, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia, and University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

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Papakostas, G.A. (2015). Improving the Recognition Performance of Moment Features by Selection. In: Stańczyk, U., Jain, L. (eds) Feature Selection for Data and Pattern Recognition. Studies in Computational Intelligence, vol 584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45620-0_13

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  • DOI: https://doi.org/10.1007/978-3-662-45620-0_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45619-4

  • Online ISBN: 978-3-662-45620-0

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