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Enhancing Accuracy in a Touch Operation Biometric System: A Case on the Android Pattern Lock Scheme

机译:增强触摸操作生物识别系统的准确性:Android模式锁定方案的情况

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The main objective of this research study is to enhance the functionality of an Android pattern lock application by determining whether the time elements of a touch operation, in particular time on dot (TOD) and time between dot (TBD), can be accurately used as a biometric identifier. The three hypotheses that were tested through this study were the following–H1: there is a correlation between the number of touch stroke features used and the accuracy of the touch operation biometric system; H2: there is a correlation between pattern complexity and accuracy of the touch operation biometric system; H3: there is a correlation between user training and accuracy of the touch operation biometric system. Convenience sampling and a within-subjects design involving repeated measures were incorporated when testing an overall sample size of 12 subjects drawn from a university population who gave a total of 2,096 feature extracted data. Analysis was done using the Dynamic Time Warping (DTW) Algorithm. Through this study, it was shown that the extraction of one-touch stroke biometric feature coupled with user training was able to yield high average accuracy levels of up to 82%. This helps build a case for the introduction of biometrics into smart devices with average processing capabilities as they would be able to handle a biometric system without it compromising on the overall system performance. For future work, it is recommended that more work be done by applying other classification algorithms to the existing data set and comparing their results with those obtained with DTW.
机译:该研究的主要目的是通过确定触摸操作的时间元素,在点(TBD)之间的特定时间和时间(TBD)之间的特定时间,可以准确地使用Android模式锁定应用程序的功能。生物识别标识符。通过该研究测试的三个假设是以下 - H1:使用的触摸行程特征数与触摸操作生物识别系统的精度之间存在相关性; H2:触摸操作生物识别系统的图案复杂性和精度之间存在相关性; H3:用户训练与触摸操作生物识别系统的准确性之间存在相关性。当测试从提供2,096个特征提取数据的大学人口中汲取的12个受试者的整体样本大小的整体样本大小并入了涉及重复措施的方便采样和受试者内部设计。使用动态时间翘曲(DTW)算法进行分析。通过本研究,表明,与用户训练耦合的单触动冲程生物识别特征的提取能够产生高达82%的高平均精度水平。这有助于建立将生物识别性引入生物识别性的情况,以平均处理能力,因为它们能够处理生物识别系统,而不会损害整体系统性能。对于未来的工作,建议通过将其他分类算法应用于现有数据集并将其结果与DTW获得的那些进行比较来完成更多的工作。

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