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When using biometric technology in forensic applications, it is necessary to compute a Log-likelihood Ratio (LLR) for a given piece of evidence (E) under two competing hypotheses, namely the prosecution and the defence hypotheses. Although LLR is a quantity expressing uncertainty and intuitively quantifying its uncertainty would not make sense, in practice, it is computed under a set of assumptions...
Automatically recognizing humans using their biometric traits such as face and fingerprint will have very important implications in our daily lives. This problem is challenging because biometric traits can be affected by the acquisition process which is sensitive to the environmental conditions (e.g., lighting) and the user interaction. It has been shown that post-processing the classifier output,...
Semi-supervised learning has been shown to be a viable training strategy for handling the mismatch between training and test samples. For multimodal biometric systems, classical semi-supervised learning strategies such as self-training and co-training may not have fully exploited the advantage of a multimodal fusion, notably due to the fusion module. For this reason, we explore a novel semi-supervised...
In order to render a biometric system robust against malicious tampering, it is important to understand the different types of attack and their impact as observed by the liveness and matching scores. In this study, we consider zero-effort impostor attack (referred to as the Z-attack), nonzero-effort impostor attack such as presentation attack or spoofing (S-attack), and other categories of attack...
Facial imaging has been largely addressed for automatic personal identification, in a variety of different environments. However, automatic face recognition becomes very challenging whenever the acquisition conditions are unconstrained. In this paper, a picture-specific cohort normalization approach, based on polynomial regression, is proposed to enhance the robustness of face matching under challenging...
Face pair matching is the task of deciding whether or not two face images belong to the same person. This has been a very active and challenging topic recently due to the presence of various sources of variation in facial images, especially under unconstrained environment. We investigate cohort normalization that has been widely used in biomet-ric verification as means to improve the robustness of...
There is mounting evidence about the benefit of tailoring a biometric authentication system to each user by postprocessing the system output at the score level, also known as client-specific score normalisation. Examples of these procedures are Z-norm and F-norm. These procedures can calibrate the uneven hypothesis space such that the dispropotionate false acceptance and false rejection errors are...
Video-based biometric systems are becoming feasible thanks to advancement in both algorithms and computation platforms. Such systems have many advantages: improved robustness to spoof attack, performance gain thanks to variance reduction, and increased data quality/resolution, among others. We investigate a discriminative video-based score-level fusion mechanism, which enables an existing biometric...
The performance of biometric systems can be significantly affected by changes in signal quality. In this paper, two types of changes are considered: change in acquisition environment and in sensing devices. We investigated three solutions: (i) model-level adaptation, (ii) score-level adaptation (normalisation), and (iii) the combination of the two, called “compound” adaptation. In order to cope with...
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