Biometrics is an emerging field of technology using unique and measurable physical, or behavioral characteristics like hand geometry, fingerprints, iris, face, gait, signature or voice that can be processed automatically to identify or verify a person. Multimodal biometric systems use more than one characteristic which are looked to as a means of reducing false non-match and false match rates, providing a secondary means of enrollment and authentication, and combating attempts to spoof biometric systems. But the major issue in multimodal biometric system is to combine the features captured from different biometric sources. Combining the features extracted from different source is called fusion. 
This can be performed in different level of biometric authentication process. As each level of fusion makes use of different type and size of information, the system accuracy and throughput are affected by the same. So, choosing appropriate fusion technique in appropriate fusion level is a challenging task. Another challenge with multimodal biometric is managing large database. Because the size of the database, not only affect the response time, but also the accuracy of the system. Thus for larger applications it is essential to prune the database to a smaller fraction which would not only ensure higher speeds, but also aid in achieving higher accuracy. Biometric data does not have a natural sorting order like traditional databases index which make indexing the biometric database a challenging problem.
The goal of the present research is to design and develop an accurate, efficient and robust system to authenticate users for a secure access mechanism in any large organization. The system would be developed with three biometric traits, namely fingerprint, face and iris of persons. We will address the limitations in the existing biometric systems such as inaccuracy due to noisy samples, poor performance due to higher computations and unreliable due to instability in traits etc. The project would also address for an efficient indexing technique to access a large database of biometric features.