MULTIMODAL AUTHENTICATION SYSTEMS: A CONSIDERATION OF SYSTEM INTEGRITY, AVAILABILITY AND RESILIENCE AGAINST SPOOFING ATTACKS

Abstract

User authentication is a fundamental requirement of any role-based access control system, governing both physical and digital access to organizational resources, and the related security and privacy of data and transactional meta-data. In our paper we review methods of authentication based on biometric characteristics such as fingerprint, retina, hand geometry, face geometry, face thermogram, voice and handwriting. We replicated recent work on multimodal biometric authentication, using aligned streams of audio and video data, and examined obfuscation techniques that could be used to undermine confidence in those techniques. Based on this experience, we designed and implemented a system for combined face and voice authentication using the open-access SpeakingFaces dataset. Vocal features are extracted using Mel-Frequency Cepstral Coefficients (MFCCs), and facial features are obtained with Local Binary Patterns (LBPs). Face and voice identification are performed using image similarity with the Euclidean distances metric and Gaussian Mixture Model (GMM) respectively, and in turn combined into a single multimodal system using matching scores fusion. The multimodal biometric authentication system was assessed using open-source data from Georgia Tech Face Database and the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus. The confidentiality of the face and voice recognition system was analyzed with several scenarios using spoofing of facial features, imitation of voice features, combined spoofing, and no spoofing scenarios.This project successfully replicated the published work, improved the computational performance and demonstrated that the ranking based model of multimodal biometric system is more resilient than a threshold-based system. Reported weaknesses of the prior works were used to improve the performance of our multimodal biometric authentication system.