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首页> 外文期刊>IAENG Internaitonal journal of computer science >Fusion Framework for Multimodal Biometric Person Authentication System
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Fusion Framework for Multimodal Biometric Person Authentication System

机译：多模式生物特征个人认证系统的融合框架

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摘要

In recent years Biometric based Authentication systems have gained more attention due to frequent fraudulent attacks. Hence, this study aims at developing a multi-modal, multi-sensor based Person Authentication System (PAS) using Joint Directors of Laboratories (JDL) fusion model. This study investigates the need for multiple sensors, multiple recognition algorithms and multiple fusion levels and their efficiency for a Person Authentication System (PAS) with face, fingerprint and iris biometrics. The proposed system considers several environmental factors in the design. If one sensor is not functional, others contribute to the system making it fault-tolerant. Robustness has been tactfully administered to the processing module by employing different efficient algorithms for a given modality. Selection of the recognition algorithms is rooted on the attributes of the input and multiplicity has been employed to establish a unanimous decision. Information fusion at various levels has been introduced. A multitude of decisions are fused locally to decide the weight for a particular modality. Algorithms are tagged with weights based on their recognition accuracy. Weights are assigned to sensors based on their identification accuracy. Adaptability is incorporated by modifying the weights based on the environmental conditions. All local decisions are then combined to result in a global decision about the person. The final aggregation concludes whether 'The Person is Authenticated or not'.
机译：近年来，由于频繁的欺诈攻击，基于生物特征的身份验证系统得到了越来越多的关注。因此，本研究旨在使用实验室联合主管（JDL）融合模型开发基于多模式，多传感器的人员身份验证系统（PAS）。这项研究调查了具有面部，指纹和虹膜生物特征识别的人认证系统（PAS）对多个传感器，多种识别算法和多种融合级别的需求及其效率。拟议的系统在设计中考虑了几个环境因素。如果一个传感器不起作用，则其他传感器对系统有所贡献，从而使其具有容错能力。通过针对给定的模式采用不同的有效算法，已将鲁棒性巧妙地赋予处理模块。识别算法的选择基于输入的属性，并且已经采用多重性来建立一致的决策。已经引入了各个级别的信息融合。许多决策在本地融合在一起，以决定特定模式的权重。根据算法的识别准确度为其标记权重。权重根据其识别精度分配给传感器。通过根据环境条件修改权重来引入适应性。然后将所有本地决策组合在一起，以得出有关此人的全局决策。最终汇总将得出“此人是否已通过身份验证”的结论。

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