Comparison Fusion of Iris and Fingerprint Traits for Personal Authentication using Artificial Neural Network with Previous Algorithm

Biometrics is the science of determining the identity of a person based on the physiological / behavioral characteristics of the individual. A person can be identified by using biometrics based on ‘what you are’ rather than ‘what you possess’ such as ID card or ‘what you remember’ such as password . Biometrics are incorporated in many different applications because of the need for reliable user authentication techniques has increased in the wake of heightened concerns about security, and rapid advances in communication, networking and mobility . A variety of biometric characteristics including face, fingerprint, palm print, iris, retina, signature, gait, ear, hand vein, voice pattern, odor or DNA are being used in various applications. Each biometric has its merits and demerits. Therefore, the selection of a biometric trait depends on several issues other than matching performance

“Integrating Iris and Fingerprint Traits for Personal Authentication using Artificial Neural Network”

In recent years, biometric based security systems chieved more attention due to continuous terrorism threats around the world. However, a security system comprised of a single form of biometric information cannot fulfil user’s expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process. In order to take full advantage of the multimodal approaches, an effective fusion scheme is necessary for combining information from various sources. I present a new methodology based on fusion at the feature level, which is a relatively new approach compared to others, to combine multimodal biometric information from two biometric identifiers (Iris and Fingerprint).The proposed system is for multimodal database comprising of 21 samples. The performance of the system is tested on a database prepared to find accuracy, false acceptance rate and false rejection rate