Weighted score level fusion of iris and face to identify an individual

Biometric authentication has become a popular and required approach to increase dynamism and security of shared information or place. But single modality fails to fulfill the present demand of accuracy and security in some cases. That's why fusion of multimodal biometrics are used to improve the identification efficiency. This paper also deals with the development of such a weighted score level fusion technique by consolidating the significance of human iris and face. Here the algorithms developed by Daugman has been used for Iris recognition process and PCA has been used for the extraction and representation of the features of human face. Finally individual iris and face matching score have been merged using weighted sum rule. Identification of the individual is confirmed based on the comparison with the weighted score. The developed multimodal technique improves both the recognition accuracy and robustness of the system.