Abstract: Biometric recognition is the automated recognition of individuals based on their behavioral or biological characteristics. Beside forensic applications, this technology aims at replacing the outdated and attack prone, physical and knowledge-based, proofs of identity. Choosing one biometric characteristic is a tradeoff between universality, acceptability, and permanence, among other factors. Moreover, the accuracy cap of the chosen characteristic may limit the scalability and usability for some applications. The use of multiple biometric sources within a unified frame, i.e. multi-biometrics, aspires to tackle the limitations of single source biometrics and thus enables a wider implementation of the technology. This work aims at presenting application-driven advances in multi-biometrics by addressing different elements of the multi-biometric system work-flow.
At first, practical oriented pre-fusion issues regarding missing data imputation and score normalization are discussed. This includes presenting a novel performance anchored score normalization technique that aligns certain performance-related score values in the fused biometric sources leading to more accurate multi-biometric decisions when compared to conventional normalization approaches. Missing data imputation within score-level multi-biometric fusion is also addressed by analyzing the behavior of different approaches under different operational scenarios.
Within the multi-biometric fusion process, different information sources can have different degrees of reliability. This is usually influenced in the fusion process by assigning relative weights to the fused sources. This work presents a number of weighting approaches aiming at optimizing the decision made by the multi-biometric system. First, weights that try to capture the overall performance of the biometric source, as well as an indication of its confidence, are proposed and proved to outperform the state-of-the-art weighting approaches. The work also introduces a set of weights derived from the identification performance representation, the cumulative match characteristics. The effect of these weights is analyzed under the verification and identification scenarios.
To further optimize the multi-biometric process, information besides the similarity between two biometric captures can be considered. Previously, the quality measures of biometric captures were successfully integrated, which requires accessing and processing raw captures. In this work, supplementary information that can be reasoned from the comparison scores are in focus. First, the relative relation between different biometric comparisons is discussed and integrated in the fusion process resulting in a large reduction in the error rates. Secondly, the coherence between scores of multi-biometric sources in the same comparison is defined and integrated into the fusion process leading to a reduction in the error rates, especially when processing noisy data.
Large-scale biometric deployments are faced by the huge computational costs of running biometric searches and duplicate enrollment checks. Data indexing can limit the search domain leading to faster searches. Multi-biometrics provides richer information that can enhance the retrieval performance. This work provides an optimizable and configurable multi-biometric data retrieval solution that combines and enhances the robustness of rank-level solutions and the performance of feature-level solutions.
Furthermore, this work presents biometric solutions that complement and utilize multi-biometric fusion. The first solution captures behavioral and physical biometric characteristics to assure a continuous user authentication. Later, the practical use of presentation attack detection is discussed by investigating the more realistic scenario of cross-database evaluation and presenting a state-of-the-art performance comparison. Finally, the use of multi-biometric fusion to create face references from videos is addressed. Face selection, feature-level fusion, and score-level fusion approaches are evaluated under the scenario of face recognition in videos.