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Temporal Analysis of Biometric Template Update Procedures in Uncontrolled Environment

机译:非受控环境中生物识别模板更新过程的时间分析

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Self-update and co-update algorithms are aimed at gradually adapting biometric templates to the intra-class variations. These update techniques have been claimed to be effective in capturing variations occurring in medium time period but no experimental evaluations have been done in the literature to clearly show this fact. The aim of this paper is the analysis and comparison of these update techniques on the sequence of input batch of samples as available over time, specifically, in the time-span of 1.5 years. Effectiveness of these techniques have been compared in terms of capability to capture significant intra-class variations and the attained performance improvement, over time. Experiments are carried out on DIEE multi-modal dataset, explicitly collected for this aim. This dataset is publicly available by contacting the authors.
机译:自我更新和共同更新算法旨在逐步使生物识别模板适应类内变异。这些更新技术被认为可以有效地捕获中等时间段内发生的变化,但文献中没有进行实验评估以清楚地表明这一事实。本文的目的是对这些更新技术进行分析和比较,以了解随着时间推移(尤其是在1.5年的时间跨度内)可用的输入样本批次的顺序。这些技术的有效性已根据捕获随时间推移发生的重大类内差异和获得的性能改进的能力进行了比较。实验是在为此目的明确收集的DIEE多模态数据集上进行的。通过联系作者可以公开获得该数据集。

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