Entropy Optimized Palmprint Enhancement Using Genetic Algorithm and Histogram Equalization

Abstract

Palmprint identification is a subcategory of biometrics identification, which can be efficiently used to identify the people. Palmprint-based identification is currently a potential alternative to human identification method of a well known fingerprint-based identification. In order to achieve high identification accuracy, all components of the scanned palmprint image need to be enhanced, i.e. palmprint lines, textures and hand geometry features. Based on Histogram Equalization (HE), a contrast enhancement scheme named Adaptively Increasing Value Histogram Equalization (AIVHE) can be used as an enhancement technique. In AIVHE method, the enhancement is controlled by two user parameters beta and gamma. Furthermore, AIVHE method enhances the contrast but the detailed information in the palmprint is not preserved. In order to enhance the palmprint and preserve the information, Genetic Algorithm (GA) is used to optimize the value of beta and gamma based on entropy value . The experiments results show that the proposed method does enhancement and brightness preservation and thereby improves information in the palmprint. Entropy of images is used as a fitness function in this work.