Multi-biometric Template Protection on Smartphones: An Approach Based on Binarized Statistical Features and Bloom Filters

Widespread use of biometric systems on smartphones raises the need to evaluate the feasibility of protecting biometric templates stored on such devices to preserve privacy. To this extent, we propose a method for securing multiple biometric templates on smartphones, applying the concepts of Bloom filters along with binarized statistical image features descriptor. The proposed multi-biometric template system is first evaluated on a dataset of 94 subjects captured with Samsung S5 and then tested in a real-life access control scenario. The recognition performance of the protected system based on the facial characteristic and the two periocular regions is observed equally good as the baseline performance of unprotected biometric system. The observed Genuine-Match-Rate (GMR) of 91.61% at a False-Match-Rate (FMR) of 0.01% indicates the robustness and applicability of the proposed system in everyday authentication scenario. The reliability of the system is further tested by engaging disjoint subset of users, who were tasked to use the proposed system in their daily activities for a number of days. Obtained results indicate the robustness of the proposed system to preserve user privacy while not compromising the inherent authentication accuracy without protected templates.