Privacy-Preserving Facial Recognition Using Biometric-Capsules

Indiana University-Purdue University Indianapolis (IUPUI)In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design. In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods

Biometric cryptosystem using online signatures

Biometric cryptosystems combine cryptography and biometrics to benefit from the strengths of both fields. In such systems, while cryptography provides high and adjustable security levels, biometrics brings in non-repudiation and eliminates the need to remember passwords or to carry tokens etc. In this work we present a biometric cryptosystems which uses online signatures, based on the Fuzzy Vault scheme of Jules et al. The Fuzzy Vault scheme releases a previously stored key when the biometric data presented for verification matches the previously stored template hidden in a vault. The online signature of a person is a behavioral biometric which is widely accepted as the formal way of approving documents, bank transactions, etc. As such, biometric-based key release using online signatures may have many application areas. We extract minutiae points (trajectory crossings, endings and points of high curvature) from online signatures and use those during the locking & unlocking phases of the vault. We present our preliminary results and demonstrate that high security level (128 bit encryption key length) can be achieved using online signatures