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首页> 外文期刊>Applied computational intelligence and soft computing >A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification
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A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

机译：基于支持向量机的人识别的基于支持向量机分类器的特征水平融合新算法

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摘要

Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches.
机译：最近，目睹了生物识别和超峰生物识别领域的许多进步。通常会在安全性，隐私和取证方面进行观察。即使是最好的单峰生物识别系统，也常常无法获得更高的识别率。多峰生物识别系统克服了单峰生物识别系统的各种局限性，例如非通用性，较低的错误接受率和较高的真实接受率。由于可获得多个具有相同身份的证据，因此可以实现更可靠的识别性能。本文提出的工作集中在使用指纹和虹膜的多峰生物特征识别系统上。使用基于Haar小波的技术提取虹膜和指纹的独特文本特征。开发了一种新颖的特征级融合算法，以使用马氏距离技术结合这些单峰特征。基于支持向量机的学习算法用于使用提取的特征来训练系统。验证了所提出算法的性能，并与使用CASIA虹膜数据库和真实指纹数据库的其他算法进行了比较。从仿真结果可以看出，与现有方法相比，我们的算法具有更高的识别率和更少的误剔除率。

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