Enhancing Person Identification with Score Fusion of Biometric Modalities

Kenza Chenni, N. Boukezzoula
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Abstract

Biometric systems play a vital role in identifying individuals by capitalizing on their distinctivephysical or behavioral traits. However, individual biometric techniques have limitations that can impact theaccuracy and reliability of identification. Multimodal biometric systems, which incorporate data fromseveral modalities, have evolved as a solution to these problems. There are four levels of integration intomultimodal biometric systems, but score-level fusion is seen as the most efficient. The fusion level used inthis paper was the score level. The fusion proposal was assessed using the ORL Face and IITD Iris datasets.The suggested technique can enhance the ability to identify people in various areas.
生物特征模式的分数融合增强人的识别
生物识别系统通过利用个体独特的身体或行为特征来识别个体,在识别个体方面发挥着至关重要的作用。然而,个体生物识别技术有局限性,可能会影响识别的准确性和可靠性。多模式生物识别系统结合了多种模式的数据,已经发展成为解决这些问题的一种方法。在多模态生物识别系统中有四个级别的整合,但分数级别的融合被认为是最有效的。本文使用的融合等级是分数等级。使用ORL Face和IITD Iris数据集对融合方案进行评估。建议的技术可以提高识别不同领域的人的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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