CABALA-Collaborative architectures based on biometric adaptable layers and activities
CABALA-Collaborative architectures based on biometric adaptable layers and activities
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DOI：
10.1016/j.patcog.2011.12.005
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发表时间：
2012-06-01
影响因子：
8
通讯作者：
Riccio, Daniel
中科院分区：
文献类型：
--
作者：
De Marsico, Maria;Nappi, Michele;Riccio, Daniel
缺乏沟通和动态适应工作环境往往阻碍了目前的多生物特征体系结构的子系统的稳定性能。校准阶段通常使用特定的训练集，以便（子）系统相对于良好确定的条件进行调整。在这项工作中，我们调查的模块化建设的系统根据CABALA（基于生物识别自适应层和活动的协作架构）的方法。支持不同级别的灵活性和协作。对于每个子系统的每个单个响应，系统可靠性（SRR）的计算允许解决由于不利条件（光、脏传感器等）而导致的准确度的暂时降低，通过可能拒绝可靠性差的响应或通过请求新的识别操作。子系统可以在两个层面上进行合作，既可以返回共同确定的答案，也可以共同进化以适应不断变化的条件。在第一级，单生物特征子系统实现N交叉测试协议：它们并行工作，但交换信息以达到最终响应。在更高的相互依赖性水平上，每个子系统的参数可以根据其同伴的行为进行动态优化。为了这个目的，一个额外的管理模块分析的单一结果，并在我们目前的实施，修改的可靠性程度，从每个子系统接受其未来的反应。本文探讨了这些新策略的不同组合。我们证明，随着组件协作的增加，同样会发生在整个系统的精度和识别不稳定子系统的能力。(C)2011爱思唯尔有限公司保留所有权利。
The lack of communication and of dynamic adaptation to working settings often hinder stable performances of subsystems of present multibiometric architectures. The calibration phase often uses a specific training set, so that (sub)systems are tuned with respect to well determined conditions. In this work we investigate the modular construction of systems according to CABALA (Collaborative Architectures based on Biometric Adaptable Layers and Activities) approach. Different levels of flexibility and collaboration are supported. The computation of system reliability (SRR), for each single response of each single subsystem, allows to address temporary decrease of accuracy due to adverse conditions (light, dirty sensors, etc.), by possibly refusing a poorly reliable response or by asking for a new recognition operation. Subsystems can collaborate at a twofold level, both in returning a jointly determined answer, and in co-evolving to tune to changing conditions. At the first level, single-biometric subsystems implement the N-Cross Testing Protocol: they work in parallel, but exchange information to reach the final response. At an higher level of interdependency, parameters of each subsystem can be dynamically optimized according to the behavior of their companions. To this aim, an additional Supervisor Module analyzes the single results and, in our present implementation, modifies the degree of reliability required from each subsystem to accept its future responses. The paper explores different combinations of these novel strategies. We demonstrate that as component collaboration increases, the same happens to both the overall system accuracy and to the ability to identify unstable subsystems. (C) 2011 Elsevier Ltd. All rights reserved.