Biometric Template Protection on Smartphones Using the Manifold-Structure Preserving Feature Representation

Abstract

Smartphone-based biometrics authentication has been increasingly used for many popular everyday applications such as e-banking and secure access control to personal services. The use of biometric data on smartphones introduces the need for capturing and storage of biometric data such as face images. Unlike the traditional passwords used for many services, biometric data once compromised cannot be replaced. Therefore, the biometric data not only should not be stored as a raw image but also needs to be protected such that the original image cannot be reconstructed even if the biometric data is available. The transforming of raw biometric data such as face image should not decrease the comparison performance limiting the use of biometric services. It can therefore be deduced that the feature representation and the template protection scheme should be robust to have reliable smartphone biometrics. This chapter presents two variants of a new approach of template protection by enforcing the structure preserving feature representation via manifolds, followed by the hashing on the manifold feature representation. The first variant is based on the Stochastic Neighbourhood Embedding and the second variant is based on the Laplacian Eigenmap. The cancelability feature for template protection using the proposed approach is induced through inherent hashing approach relying on manifold structure. We demonstrate the applicability of the proposed approach for smartphone biometrics using a moderately sized face biometric data set with 94 subjects captured in 15 different and independent sessions in a closed-set scenario. The presented approach indicates the applicability with a low Equal Error Rate, \\(EER = 0.65\\%\\) and a Genuine Match Rate, \\(GMR = 92.10\\%\\) at False Match Rate (FMR) of \\(0.01\\%\\) for the first variant and the second variant provides \\(EER = 0.82\\%\\) and \\(GMR = 89.45\\%\\) at FMR of \\(0.01\\%\\). We compare the presented approach against the unprotected template performance and the popularly used Bloom filter template.