Abstract

Multimodal Biometric Systems are extensively employed over unimodal counterparts for user authentication in the digital world. However, the application of multimodal systems to security-critical applications is limited mainly due to non-adaptiveness of these systems to the dynamic environment and inability to distinguish between spoofing attack and the noisy input image. In order to address these issues, a multimodal biometric system, which adaptively combines the scores from individual classifiers is proposed. For this, three modalities viz. face, finger, and iris are used to extract individual classifier scores. These classifier scores are adaptively fused considering that concurrent modalities are boosted and discordant modalities are suppressed. The conflicting belief among classifiers is resolved not only to achieve optimum fusion of classifier scores but also to cater dynamic environment. The proposed quality based score fusion also distinguish between spoofing attacks and noisy inputs as well. The performance of the proposed multimodal biometric system is experimentally validated using three chimeric multimodal databases. On an average, the proposed system achieves an accuracy of 99.5%, an EER of 0.5% and also outperforms state-of-the-art methods.

中文翻译：

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基于质量的多模态生物识别系统自适应分数融合方法

摘要

在数字世界中，多峰生物特征识别系统广泛应用于单峰对应物，以进行用户身份验证。但是，将多模式系统应用于对安全性要求很高的应用程序受到限制，主要是因为这些系统对动态环境的不适应性以及无法区分欺骗攻击和嘈杂的输入图像。为了解决这些问题，提出了一种多模式生物识别系统，该系统自适应地组合了来自各个分类器的分数。为此，有三种方式。面部，手指和虹膜用于提取单独的分类器分数。考虑到并发模态得到了增强，不协调模态得到了抑制，这些分类器分数被自适应融合。分类器之间的冲突信念得到解决，不仅实现了分类器分数的最佳融合，而且还满足了动态环境。提出的基于质量的分数融合还可以区分欺骗攻击和嘈杂的输入。拟议的多峰生物特征识别系统的性能已通过使用三个嵌合多峰数据库进行了实验验证。平均而言，所提出的系统可实现99.5％的精度，0.5％的EER，并且也优于最新方法。