Manifold-Structure Preserving Biometric Templates - A Preliminary Study On Fully Cancelable Smartphone Biometric Templates

The increasing use of smartphone biometrics proportionally increases the need to protect the biometric data. Unlike the traditional passwords, biometric data once comprised cannot be replaced. The smartphones thus need to protect the biometric templates and store the protected templates instead of the raw biometric images such as face image. The challenge in obtaining optimal biometric templates without compromising the performance also relies on preserving the structural neighbourhood of the biometric characteristics. We therefore address these two problems by employing cancelable biometric templates via hashing. The approach further is based on neighbourhood structure preserving manifold which we refer as Manifold-structure Preserving Biometric Template (MaPBiT). The proposed approach also demonstrates the properties of an ideal biometric template protection scheme and obeys irreversibility, unlinkability and renewability. In order to validate the applicability of the proposed approach, we employ a face and periocular database captured using the smartphone that consists of 94 subjects in 15 different and independent sessions. The proposed approach results in a low Equal Error Rate (≈ 0.80%) with a Genuine Match Rate (≈ 97.5% at False Match Rate of 0.01%). The results indicate the promising nature of the proposed approach as compared to the traditional unprotected templates while outperforming the bloom-filter based template protection.