Novel Feature Selection by Differential Evolution Algorithm

Iris scan biometrics employs the unique characteristic and features of the human iris in order to verify the identity of in individual. In today's world, where terrorist attacks are on therise employment of infallible security systems is a must. This makes Iris recognition systems unavoidable in emerging security.Authentication the objective function is minimized using Differential Evolutionary (DE) Algorithm where the population vector is encoded using Binary Encoded Decimal to avoid the float number optimization problem. An automatic clustering of the possible values of the Lagrangian multiplier provides adetailed insight of the selected features during the proposed DE based optimization process. The classification accuracy ofSupport Vector Machine (SVM) is used to measure the performance of the selected features. The proposed algorithm outperforms the existing DE based approaches when tested on IRIS, Wine, Wisconsin Breast Cancer, Sonar and Ionosphere datasets. The same algorithm when applied on gait based peopleidentification, using skeleton data points obtained from Microsoft Kinect sensor, exceeds the previously reported accuracies

Keywords:

iris , Feature selection , Differential Evolution Algorithm , optimization