The rising demand for high security and reliable authentication schemes, led to the development of the unimodal biometric system so that the multimodal biometric system has emerged. The multimodal biometric system will use more than one biometric trait of an individual for identification and security purpose. Fusion plays a major role in the multimodal biometric system. Several fusion techniques are used in biometric systems. Feature level fusion is a very much popular method as compared to the other fusion techniques. In this fusion, features are extracted from all biometric traits. After that extracted features are combined into a final feature vector of high dimension. In this paper, we introduce a new technique to perform fusion at the feature level by optimal feature level fusion; here the relevant features are selected using an optimization technique. Here, we proposed OGWO for selecting optimal features. Moreover, we suggested the recognition technique. For recognition, we use the multi-kernel support vector machine algorithm. Finally, the performance of our proposed method is evaluated by some evaluation measures. Our recommended method is implemented in the MATLAB platform.

对高安全性和可靠认证方案的需求不断增长，导致了单峰生物识别系统的发展，从而出现了多峰生物识别系统。多模式生物特征识别系统将使用一个人的多个生物特征，以进行识别和安全目的。融合在多模式生物识别系统中起着重要作用。在生物识别系统中使用了几种融合技术。与其他融合技术相比，特征级融合是非常受欢迎的方法。在这种融合中，将从所有生物特征中提取特征。之后，将提取的特征合并为高维的最终特征向量。在本文中，我们介绍了一种通过最佳特征级别融合在特征级别执行融合的新技术。在此，使用优化技术选择相关特征。在这里，我们提出了OGWO用于选择最佳特征。此外，我们提出了识别技术。为了识别，我们使用多核支持向量机算法。最后，通过一些评估方法对我们提出的方法的性能进行了评估。我们推荐的方法是在MATLAB平台中实现的。