A genetic algorithm for creating a set of color spaces for ear authentication


An ensemble of 2D ear matchers is built by training each matcher using a set of Gabor filters and color spaces selected by a genetic algorithm (GA). First, using gray level images, we select the best Gabor filters applying Sequential Forward Floating Selection. Second, using the RGB images, several color spaces are obtained using a GA. Finally, an ensemble of 1-nearest neighbor matchers use the color spaces and filters for classification. The performance of the proposed approach is measured using the Notre-Dame EAR dataset. To create the color spaces, the dataset is divided into training and testing sets using ear samples from different individuals. System parameters are selected using samples of individuals that belong to the training set. The method is then tested on the testing set. In this way, we consider our protocol a reliable blind testing protocol. Our system obtains rank-1 of ∼81% and rank-5 of ∼92%.


Information Technology and Cybersecurity

Document Type

Conference Proceeding


Color space, Ear verification, Floating search, Gabor filters, Multimatchers

Publication Date


Journal Title

Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009