Person Identification using Biometrics ( Iris Recognition )

Biometric-based personal verification and identification methods have gained much interest with an increasing emphasis on security. Iris recognition is a fast, accurate and secure biometric technique that can operate in both verification and identification modes since the iris texture pattern has no links with the genetic structure of an individual and since it is generated by chaotic process. In this paper, we present a method for iris recognition based on a wavelet packet decomposition of iris images. Each iris image is described by a subset of band-filtered images (sub images) containing wavelet coefficients. From these coefficients, which characterize the iris texture, we compute a compact iris feature code using the appropriate energies of these sub images to generate binary iris codes according to an adapted threshold. Thereafter, we show how an efficient and reliable Hamming distance can be used in order to classify iris codes. Results are presented that demonstrate significant improvements in iris recognition accuracy through the use of the public iris database CASIA.