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Multi-modal Biometrics System Using Face and Signature

机译:使用面部和签名的多模式生物识别系统

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摘要

In this paper, we propose a multi-modal biometrics system based on the face and signature recognition. For this, we suggest biometric algorithms for the face and signature recognition. First, we describe a fuzzy linear discriminant analysis (LDA) method for the face recognition. It is an expanded version of the Fisherface method using the fuzzy logic which assigns fuzzy membership to the LDA feature values. On the other hand, the signature recognition has the problem that its performance is often deteriorated by signature variation from various factors. Therefore, we propose a robust online signature recognition method using LDA and so-called Partition Peak Points (PPP) matching technique. Finally, we propose a fusion method for multi-modal biometrics based on the support vector machine. From the various experiments, we find that the proposed method renders higher recognition rates comparing with the single biometric cases under various situations.
机译:在本文中,我们提出了一种基于面部和签名识别的多模式生物识别系统。为此,我们建议针对面部和签名识别的生物特征识别算法。首先,我们描述了一种用于人脸识别的模糊线性判别分析(LDA)方法。它是使用模糊逻辑将Fisher模糊成员分配给LDA特征值的Fisherface方法的扩展版本。另一方面,签名识别具有以下问题:由于各种因素的签名变化,其性能通常会劣化。因此,我们提出了使用LDA和所谓的“分区峰值点”(PPP)匹配技术的可靠的在线签名识别方法。最后,我们提出了一种基于支持向量机的多模式生物特征识别融合方法。从各种实验中我们发现,与在各种情况下的单个生物特征识别案例相比,该方法具有更高的识别率。

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