Recognition of selected fingerprints and iris features enhanced by curvelet transform with Artificial Neural Networks

Biometric systems based on one-modal biometrics are often not able to meet the desired performance requirements for large user population applications, due to problems such as noisy data, intra-class variations, restricted degrees of freedom, non-university, spoof attacks, and unacceptable error rates. Therefore, multimodal biometrics refers to the use of a combination of two or more biometric modalities in a single recognition or identification system. In order to ensure that the performance of multibiometric systems such as fingerprint and iris will be powerful with respect to the quality of obtained fingerprint and iris images, these images are denoised and enhanced. In this study, curvelet transform is applied biometric images for enhancement. Obtained results after applied curvelet transform is compared to the other traditional image enhancement algorithms. Features obtained from enhanced fingerprints and iris images are selected by using genetic algorithms because of too huge dataset. Selected features are input to artificial neural networks for biometric recognition. Thus, the recognition is achieved very fast without to reduce the performance.