GA-based feature selection approach in biometric hand systems

In this paper, a novel methodology for using feature selection in hand biometric systems, based on genetic algorithms and mutual information is presented. A hand segmentation algorithm based on adaptive threshold and active contours is also applied, in order to deal with complex back grounds and non-homogeneous illumination. The aim of this methodology is two-fold. On the one hand, getting robust features in biometric systems with no restriction in the hand-pose and in its orientation with regard to the camera. On the other hand, providing a subset of features which reduce the complexity of the identification process and maximize the generalization rate of the classifiers. By using the IITD Palmprint Database, which is an example of such free hand-pose biometric systems, the experimental results show that it is not always necessary to apply sophisticated classification methods to obtain good accuracy results. Simple classifiers such as kNN and LDA together with this feature selection approach, get even better generalisation rates than other more elaborate and complex methods.