Journal ArticleDOI
Likelihood Ratio-Based Biometric Score Fusion
TLDR
Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.Abstract:
Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for the optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle 1) discrete values in biometric match score distributions, 2) arbitrary scales and distributions of match scores, 3) correlation between the scores of multiple matchers, and 4) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.read more
Citations
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Journal ArticleDOI
A Cumulants-Based Human Brain Decoding
Raheel Zafar,Muhammad Javvad ur Rehman,Sheraz Alam,Muhammad Arslan Khan,Asad Hussain,Rana Fayyaz Ahmad,Faruque Reza,Rifat Jahan +7 more
TL;DR: A statistical technique is offered together with specialised feature extraction and selection methods to increase the accuracy and it is compared with the current methods reported in recent research to validate the suggested approach.
Biometrics via Oculomotor Plant Characteristics: Impact
TL;DR: In this article, the anatomical properties of the human oculomotor plant from the measurable properties of human eye movements are estimated using a two-dimensional linear homeomorphic model of the oculOMotor plant.
DissertationDOI
Mitigating the effect of covariates in face recognition
TL;DR: To address the challenge of facial aging, an age transformation algorithm is proposed that registers two face images and minimizes the aging variations and the concept of online learning is introduced to address the problem of classifier re-training and update.
Journal ArticleDOI
Hierarchical Representation Learning for Kinship Verification
TL;DR: The results show that the proposed deep learning framework (KVRL-fcDBN) yields state-of- the-art kinship verification accuracy on the WVU Kinship database and on four existing benchmark datasets.
Proceedings ArticleDOI
Redundancy and diversity measure inspired biometrics fusion
TL;DR: A novel solution which uses a machine learning approach to generate confidence scores for each system for every instance of decision by implicitly modelling the redundancy and diversity measures from the training data.
References
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Journal ArticleDOI
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