Development of a robust triple multimodal biometric approach for human authentication using fingerprint, iris and voice biometric is the main objective of this manuscript. Accordingly, three essential algorithms for biometric authentication are presented. The extracted features from these multimodals are combined via feature fusion center (FFC) and feature scores. These features are trained through artificial neural network (ANN) and support vector machine (SVM) classifiers. The first algorithm depends on boundary energy method (BEM) extracted features from fingerprint, normalized combinational features from iris and dimensionality reduction methods (DRM) from voice using sum/average FFC. The second proposed algorithm uses extracted features from zoning method of fingerprint, SIFT of iris and higher order statistics (HOS) of voice signals. The third proposed algorithm consists of extracted features from zoning method for fingerprint, SIFT from iris and DRM from voice signals. Classification accuracy of implemented algorithms is estimated. Comparison between proposed algorithms is introduced in terms of equal error rate (EER) and ROC curves. The experimental results confirm superiority of second proposed algorithm which achieves a classification rate of 100% using SVM classifier and sum FFC. From computational point of view, the first algorithm consumes the lowest time using SVM classifier. On other hand, the lowest EER is achieved by first proposed algorithm for extracted features from Karhunen-Loeve transform (KLT) method of DRM. Additionally, the lowest ROC curves are accomplished respectively for extracted features from multidimensional scaling (MDS), generated ARMA synthesis and Isomap features. Their accuracy is improved with SVM. Also, the sum FFC introduces efficient results compared to average FFC. These algorithms have the advantages of robustness and the strength of selecting unimodal, double and triple biometric authentication. The obtained results accomplish a remarkable accuracy for authentication and security within multi practical applications.

中文翻译：

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使用指纹，虹膜和语音特征融合的鲁棒多峰生物特征认证算法

本手稿的主要目的是开发一种可靠的三重多模态生物特征识别方法，用于使用指纹，虹膜和语音生物特征识别的人体。因此，提出了三种用于生物特征认证的基本算法。从这些多模态中提取的特征通过特征融合中心（FFC）和特征分数进行组合。这些功能通过人工神经网络（ANN）和支持向量机（SVM）分类器进行训练。第一种算法取决于使用总和/平均FFC从指纹提取的边界能量方法（BEM），从虹膜提取的归一化组合特征以及从语音中提取的降维方法（DRM）。提出的第二种算法使用了从指纹分区方法，虹膜SIFT和语音信号高阶统计量（HOS）中提取的特征。提出的第三种算法由指纹分区方法中提取的特征，虹膜中的SIFT和语音信号中的DRM组成。估计已实现算法的分类精度。根据等错误率（EER）和ROC曲线介绍了所提出算法之间的比较。实验结果证实了第二种算法的优越性，该算法使用SVM分类器和总和FFC可以达到100％的分类率。从计算的角度来看，第一种算法使用SVM分类器消耗的时间最少。另一方面，通过从DRM的Karhunen-Loeve变换（KLT）方法中提取特征的第一个提出算法可以实现最低的EER。此外，对于从多维缩放（MDS）提取的特征，分别完成了最低的ROC曲线，生成的ARMA综合和Isomap功能。使用SVM可以提高其准确性。同样，与平均FFC相比，总FFC引入了有效的结果。这些算法具有健壮性和选择单峰，双重和三重生物特征认证的优势。所获得的结果在多种实际应用中实现了卓越的身份验证和安全性。