Investigation of information fusion in face and palmprint multimodal biometrics

Nurain Mohamad, Muhammad Imran Ahmad, R. Ngadiran, M. Z. Ilyas, M. I. N. Isa, Puteh Saad
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引用次数: 4

Abstract

This paper reviews several information fusion techniques and strategies in the application of multimodal biometrics system using face and palmprint images. Multimodal biometric is able to overcome several limitations in single modal biometric such as intra-class variations, less discriminative power, noise data and redundant features. By consolidating two kinds of modality a better performance can be achieved. Information fusion in multimodal biometrics can be carried out at three possible levels, i.e. feature, matching score and decision levels. Fusions at these three levels have their own attributes, thus this paper is aimed to compare their effectiveness. A specific fusion rule is necessary to combine the information at each level. Several numbers of analyses on verification and identification shows matching score fusion is able to achieve the best performance which is 98% recognition rates and 98.5% GAR at 0.1% FAR when tested using AR face and PolyU palmprint datasets.
人脸与掌纹多模态生物特征信息融合研究
本文综述了几种信息融合技术和策略在基于人脸和掌纹图像的多模态生物识别系统中的应用。多模态生物识别技术能够克服单模态生物识别技术的一些局限性,如类内差异、鉴别能力差、噪声数据和冗余特征。通过两种形态的整合,可以达到更好的效果。多模态生物识别中的信息融合可以在特征、匹配分数和决策三个层次上进行。这三个层次的融合都有各自的属性,因此本文旨在比较它们的有效性。需要一个特定的融合规则来组合每个级别的信息。多项验证和识别的分析显示,当使用AR人脸和理大掌纹数据集进行测试时,匹配分数融合能够达到最佳性能,在0.1% FAR下达到98%的识别率和98.5%的GAR。
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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