The probabilities for the technology to be spoofed are widely acknowledged in biometric verification system. Important efforts have been conducted to study such threats and to develop countermeasures to direct attacks to the biometric verification system to ensure the security of these systems against spoof attacks and reduce this risk, by using another module that is added to the biometric verification system called the "liveness detection" which uses different anatomical properties to distinguish between real and fake traits. Thus, the robustness of the system against direct attacks can be improved through increasing the security level offered to the final user. This paper is an attempt to construct support biometric security system to protect the iris biometric verification system from spoof attacks, through integrating the iris verification system with addition module called liveness detection which composed of two sub-modules (static and dynamic). A test has been performed, for iris verification phase performed on two types of database (MMU DB) for 180 samples and (CASIA DB) for 90 samples, and gave accuracy (99.44%) with FAR of (0.0277) and FRR (0.0055) for MMU DB, and accuracy (97.77%) with FAR of (0.0333) and FRR (0.0222) for CASIA DB.

The probabilityof the biometric system to be spoofed is widely acknowledged. Complete security does not really exist, butsignificant efforts have led to study such threats and to develop countermeasures to direct attacks to the biometric system in an attempt to ensure the security and to reduce this risk. This paper presents two novel anti-spoofing techniques to protect iris biometric system from spoof attack, static and dynamic. Static technique is based on the principle of degree of sharpening of the input eye image. Dynamic technique is based on variation of the size of the pupil if the illumination is increased. This technique is tested on 15 folders of original MMU database (Multi Media University database) Each folder contains two eyes image sampleswhich represent live trail and 15 folders of (MMU database) eye images printed using scanner device and photographed using a specific camera are saved in computer to represent 15 attempts of spoof attack. The evaluation tests of liveness detection phase for iris which is applied in iris database show that the detection of the liveness properties is very good as depicted in Table (1) and Table (2).