Abstract—This paper presents an efficient approach for classification of the gender of a common fruit fly, Drosophila melanogaster, based on their wing’s texture. The novelty of this research effort is that a Modified Local Binary Pattern (MLBP), which combines both the sign and magnitude features for the improvement of fly wing’s texture classification performance, is applied. The extracted features are then used to classify the gender of the fruit fly by using the Support Vector Machines (SVMs) and Random Forest (RF). We validate the performance of the proposed scheme on two fly wing datasets. The highest accuracy achieved by the proposed approach is 94%. In this paper, we limit our approach to gender classification; however, this effort can be extended to explore important characteristics of a fly using wing’s texture analysis.
Index Terms—Biometrics, fly wing, modified local binary pattern, random forest, and support vector machine.
Foysal Ahmad, Kaushik Roy, Brian O’Connor, Joseph Shelton, and Gerry Dozier are with the North Carolina A&T State University, Greensboro, NC 27411 USA (e-mail: fahmad@aggies.ncat.edu; kroy@ncat.edu; bpoconno@aggies.ncat.edu; jashelt1@aggies.ncat.edu; gvdozier@ncat.edu).
Ian Dworkin is with Michigan State University, East Lansing, Michigan 48824 (e-mail: idworkin@msu.edu).
Cite: Foysal Ahmad, Kaushik Roy, Brian O’Connor, Joseph Shelton, Gerry Dozier, and Ian Dworkin, "Fly Wing Biometrics Using Modified Local Binary Pattern, SVMs and Random Forest," International Journal of Machine Learning and Computing vol.4, no. 3, pp. 279-285, 2014.