Feature level fused templates for multi-biometric system on smartphones

This work examines feature level fusion for protected biometric templates in a multi-biometric authentication system for smartphones. The modalities incorporated by the system are face and the left-right periocular region. The fusion methods considered are concatenation of the templates obtained from the three modalities, and combining the three templates using a simple XOR operation by varying the amount of overlap between them up to 100%. The impact on performance from applying the feature level fusion methods are evaluated on a moderate sized dataset consisting of images from 73 subjects, captured using a Samsung Galaxy S5. We show that the biometric performance can be improved in most of the cases by employing the fusion methods when compared to the performance of each individual modality while not compromising the security level provided by template protection schemes.