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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/u3qqmkiofjejrnpdxh3hdgssm4" style="color: black;">Information Fusion</a>
26 This paper presents a research for the use of multi-source information fusion in the field of eye 27 movement biometrics. In the current state-of-the-art, there are different techniques developed to 28 extract the physical and the behavioral biometric characteristics of the eye movements. In this work, 29 we explore the effects from the multi-source fusion of the heterogeneous information extracted by 30 different biometric algorithms under the presence of diverse visual stimuli. We propose<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.inffus.2015.08.003">doi:10.1016/j.inffus.2015.08.003</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uk5mpcsakfbahfvnocgtc7qmve">fatcat:uk5mpcsakfbahfvnocgtc7qmve</a> </span>
more »... two-stage 31 fusion approach with the employment of stimulus-specific and algorithm-specific weights for fusing 32 the information from different matchers based on their identification efficacy. The experimental 33 evaluation performed on a large database of 320 subjects reveals a considerable improvement in 34 biometric recognition accuracy, with minimal equal error rate (EER) of 5.8%, and best case Rank-1 35 identification rate (Rank-1 IR) of 88.6%. It should be also emphasized that although the concept of 36 multi-stimulus fusion is currently evaluated specifically for the eye movement biometrics, it can be 37 adopted by other biometric modalities too, in cases when an exogenous stimulus affects the extraction 38 of the biometric features. 39 40
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