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Multibiometric People Identification: A Self-tuning Architecture

机译:多学会人员识别:自我调整架构

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Multibiometric systems can solve a number of problems of unimodal approaches. One source for such problems can be found in the lack of dynamic update of parameters, which does not allow current systems to adapt to changes in the working settings. They are generally calibrated once and for all, so that they are tuned and optimized with respect to standard conditions. In this work we propose an architecture where, for each single-biometry subsystem, parameters are dynamically optimized according to the behaviour of all the others. This is achieved by an additional component, the supervisor module, which analyzes the responses from all subsystems and modifies the degree of reliability required from each of them to accept the respective responses. The paper explores two integration architectures with different interconnection degree, demonstrating that a tight component interaction increases system accuracy and allows identifying unstable subsystems.
机译:多学术系统可以解决一些单向方法的问题。可以在缺乏动态更新参数的情况下找到此类问题的一个来源,这不允许当前系统适应工作设置中的更改。它们通常是一次校准一次,以便在标准条件下调谐和优化它们。在这项工作中,我们提出了一种架构,对于每个单一生物系统,参数,参数根据所有其他的行为动态优化。这是通过附加组件,主管模块来实现的,该主管模块分析了所有子系统的响应并修改了每个子系统的响应,从而修改了每个子系统所需的可靠性以接受各个响应。本文探讨了两个具有不同互连度的集成架构,展示了紧密的组件交互会提高系统精度并允许识别不稳定的子系统。

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