Fly wing biometrics

Author

Payne, Michael ; Turner, Jessica ; Shelton, Joseph ; Adams, J. ; Carter, Jenny ; Williams, Henry ; Hansen, Charles ; Dworkin, I. ; Dozier, Gerry

Author_Institution

Dept. of Comput. Sci., North Carolina A&T State Univ., Greensboro, NC, USA

fYear

2013

fDate

16-19 April 2013

Firstpage

42

Lastpage

46

Abstract

Genetic and Evolutionary Feature Extraction (GEFE), introduced by Shelton et al. [1], [2], [3], use genetic and evolutionary computation to evolve Local Binary Pattern (LBP) based feature extractors for facial recognition. In this paper, we use GEFE in an effort to classify male and female Drosophila melanogaster by the texture of their wings. To our knowledge, gender classification of the drosophila melanogaster via its wing has not been performed. This research has the potential to simplify the work of geneticists who work with the drosophila melanogaster. Our results show that GEFE outperforms both LBP and Eigenwing methods in terms of accuracy as well as computational complexity.

Keywords

biology computing; computational complexity; feature extraction; genetic algorithms; image classification; zoology; GEFE; computational complexity; evolutionary computation; evolutionary feature extraction; female Drosophila melanogaster classification; fly wing biometrics; gender classification; genetic computation; genetic feature extraction; local binary pattern; wings texture; Biometrics (access control); Feature extraction; Genetics; Iron; Probes; Sociology; Statistics; Biometrics; Drosophila; Feature Extraction; Genetic Algorithms (GA); Genetic and Evolutionary Computations (GECs); Genetic and Evolutionary Feature Extraction (GEFE); Local Binary Pattern (LBP);

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Biometrics and Identity Management (CIBIM), 2013 IEEE Workshop on

Conference_Location

Singapore

ISSN

2325-4300

Type

conf

DOI

10.1109/CIBIM.2013.6607912

Filename

6607912