Robust Algorithms for Audio-Visual Biometric Authentication

Biometric authentication has been a vital method of human identification across a wide range of applications. It is a quick and secure authorization process in applications such as border control and banking transactions. Recent advances in technology have triggered the use of embedded biometrics in smartphones and handheld devices. For reliable authentication, biometrics perform better when compared to traditional techniques such as passwords. Moreover, its advantages, such as permanence and uniqueness, have increased the growth of biometrics in everyday usage. However, there are certain limitations to biometric systems in providing optimal performance. These limitations play a crucial role in formulating artefacts to conceal or recreate the identity of individuals. Therefore, this raises concerns about the robustness of a biometric system and questions the accuracy of biometric recognition. In a real-world scenario, an end-user biometric system is protected from tampering by external sources. However, the only source of interaction is through the data capturing sensor. Thus, external artefacts, namely presentation attacks, have been a severe threat to biometric systems. On the other hand, the internal dependencies typically come from the limitations of the hardware and software deployed in the biometric verification workflow. Examples of internal dependencies vary from noise in biometric data to dependencies of biometric algorithms. In this thesis, we have focused on improving the generalization of biometrics by working on some of the problems caused by presentation attacks and internal dependencies in biometrics. The key challenges in audio-visual biometrics were identified, and research objectives were designed for this thesis. The vulnerabilities in audio-visual biometrics are observed with the help of a thorough review of existing recognition and presentation attack detection methods. An exhaustive and comprehensive study along with a comparison and categorization of state-of-the-art methods have resulted in a novel dataset. The dataset includes different attributes, which provide the scope to perform extensive experiments to understand dependencies and vulnerabilities. The thesis proposes a fusion of texture features based iris presentation attack detection algorithm, with results showing superior performance. Further, the cross-dataset experiments led to an empirical evaluation of vulnerabilities in iris biometrics due to presentation attacks. In speaker recognition research, voice impersonation, language dependency and audio replay attacks cause high vulnerability. This thesis proposes a novel voice impersonation dataset with three different languages. The impact of voice impersonation as an attack is evaluated on the state-of-the-art speaker verification methods. Further, the speaker’s language mismatch in the enrollment and testing steps of speaker verification is examined. The recent progress in smartphone usage is also reflected in high-quality speakers and microphones. A replay attack dataset is created with multiple smartphones as playback and recording devices, and vulnerability is examined. The thesis examines the generalizability of biometric algorithms to improve the robustness of biometric recognition. The results from the proposed methods are evaluated with extensive experiments and detailed examinations of both publicly available databases and new datasets created in this work. In conclusion, the thesis proposes novel methods and approaches to examining the vulnerabilities in audio-visual biometrics, presentation attack detection (PAD) in iris and voice biometrics, the study of language dependency and audio replay attacks. The methods presented are valuable contributions to the research fields in developing robust smartphone biometric methods by addressing vulnerabilities from multiple sources