Efficient cost-sensitive human-machine collaboration for offline signature verification
Efficient cost-sensitive human-machine collaboration for offline signature verification
复制标题
高效、成本敏感的人机协作，用于离线签名验证
DOI：
10.1117/12.910460
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
R. Sabourin
中科院分区：
文献类型：
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作者：
J. Coetzer;Jacques P. Swanepoel;R. Sabourin
我们提出了一种新的策略，在成本敏感的环境中的人机决策的最佳组合。所提出的算法应该是特别有益的金融机构，离线签名，每个与特定的交易价值，需要认证。当呈现由所谓的豚鼠作者产生的真实和欺骗性训练签名的集合时，可以估计人类雇员和分数生成机器的劳动力的熟练度，并在接收器操作特性（ROC）空间中表示。使用一组布尔融合函数，将人类劳动力的多数投票决策与每个阈值特定的机器生成的决策相结合。候选集合的性能被估计并表示在ROC空间中，之后仅保留最优集合和相关联的决策树。当呈现与任意作者链接的有疑问的签名时，系统首先使用与交易值相关联的基于ROC的成本梯度来选择使预期成本最小化的系综，然后使用相应的决策树来认证有疑问的签名。我们表明，当利用整个人类劳动力时，机器的加入简化了认证过程，并降低了所有操作条件下的预期成本。
We propose a novel strategy for the optimal combination of human and machine decisions in a cost-sensitive environment. The proposed algorithm should be especially beneficial to financial institutions where off-line signatures, each associated with a specific transaction value, require authentication. When presented with a collection of genuine and fraudulent training signatures, produced by so-called guinea pig writers, the proficiency of a workforce of human employees and a score-generating machine can be estimated and represented in receiver operating characteristic (ROC) space. Using a set of Boolean fusion functions, the majority vote decision of the human workforce is combined with each threshold-specific machine-generated decision. The performance of the candidate ensembles is estimated and represented in ROC space, after which only the optimal ensembles and associated decision trees are retained. When presented with a questioned signature linked to an arbitrary writer, the system first uses the ROC-based cost gradient associated with the transaction value to select the ensemble that minimises the expected cost, and then uses the corresponding decision tree to authenticate the signature in question. We show that, when utilising the entire human workforce, the incorporation of a machine streamlines the authentication process and decreases the expected cost for all operating conditions.