A multi-modal and multi-algorithmic biometric system combining iris and face

In this paper, we have developed an algorithm which combines features from human iris and face for person verification. Iris recognition is one of the most accurate biometric modalities having verification results close to 98%. On the other hand, face is one of the most widely used biometric features because of its ease of capture. We have adapted score level fusion strategy for our system. However, in addition to this, we are using two different features for face: Gabor filters based and Local Binary Patterns (LBP) based. The iris features are extracted using Daugman's Gabor filters based approach. Using this information, we have developed a multi-modal (combining iris and face), multi-algorithmic (using two different algorithms for feature extraction from face) biometric system. With this system, we achieved more than 85% improvement in the verification performance in terms of Equal Error Rate as compared to the uni-biometrics based system.