A new approach is proposed to quantitatively evaluate the binary detection performance of the biometric personal recognition systems. The importance of correlation between the overall detection performance and the area under the genuine acceptance rate (GAR) versus false acceptance rate (FAR) graph, commonly known as receiver operating characteristics (ROC) is recognized. Using the ROC curve, relation between GARmin and minimum recognition accuracy is derived, particularly for high security applications (HSA). Finally, effectiveness of any binary recognition system is predicted using three important parameters, namely GARmin, the time required for recognition and computational complexity of the computer processing system. The palm print (PP) modality is used to validate the theoretical basis. It is observed that by combining different useful feature-extraction techniques, it is possible to improve the system accuracy. An optimum algorithm to appropriately choose weights has been suggested, which iteratively enhances the system accuracy. This also improves the effectiveness of the system.

中文翻译：

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迭代权重优化算法提高生物识别的准确性

提出了一种新方法来定量评估生物特征个人识别系统的二进制检测性能。公认的是，整体检测性能与真实接受率（GAR）与错误接受率（FAR）图下的面积（通常称为接收器工作特性（ROC））之间相关性的重要性。使用ROC曲线，可以得出GARmin与最小识别精度之间的关系，特别是对于高安全性应用（HSA）。最后，使用三个重要参数预测任何二进制识别系统的有效性，即GARmin，识别所需的时间和计算机处理系统的计算复杂性。掌纹（PP）方式用于验证理论基础。可以看出，通过组合使用各种有用的特征提取技术，可以提高系统精度。最佳算法来选择适当的权重已建议，其中反复提高了系统精度。这也提高了系统的有效性。