指纹质量与皮肤属性之间相关性的证据

R. Hancock, S. Elliott
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引用次数: 0

摘要

本文的目的是寻找指纹质量与皮肤质地、角蛋白水平、皮肤色素沉着、皮肤颜色、皮肤温度、弹性、手指细节等因素是否存在相关性的证据。简单地说,目标是看看手指特征是否以及哪些特征会影响指纹的可读性。为了实现这一目标,从80个不同受试者的手指上随机收集了大约8000个样本。传感器收集的数据包括皮肤纹理、角蛋白水平、皮肤色素沉着、皮肤颜色、温度、弹性和手指上的细微差别。传感器还收集每个指纹的图像质量。该测量与指纹扫描仪的有效性高度相关,因此在实验中用作指纹可读性的表示。在Minitab中对上述因素和图像质量进行了最佳子集测试。这个函数测试了所有可能的线性模型,这些模型可以通过结合图像质量的因素来创建,并给出2个结果。第一个结果是确定的最佳模型,第二个结果是告诉用户模型的有效性的统计数据。将除色素沉着外的所有因素都考虑在内的模型作为最佳模型。然而,该模型的R2值仅为2.4,这意味着该模型只能解释2.4%的图像质量数据。这提供了强有力的证据,表明这些因素与指纹图像质量之间没有线性关系,因此指纹扫描仪的有效性。为了解决因素和图像质量之间的非线性关系的可能性,每个因素都绘制在图像质量图上。如果变量与图像质量呈非线性关系,则会在图上出现一个模式。在任何图表上都没有出现令人信服的模式,这证明手指因素和图像质量之间也没有非线性关系。这与之前关于线性关系的发现相结合,使我们能够声明,有强有力的证据表明,这些因素与指纹扫描仪的有效性无关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evidence of correlation between fingerprint quality and skin attributes
The purpose of this paper is to find whether there is any evidence of correlation between fingerprint quality and the factors of skin texture, keratin level, skin pigmentation, skin color, skin temperature, elasticity, and finger minutiae. In simpler terms, the goal was to see if and which finger characteristics affected the readability of the fingerprint. To achieve this goal, about 8000 random samples were collected from the fingers of 80 different subjects. The sensors collected data involving skin texture, keratin level, skin pigmentation, skin color, temperature, elasticity, and the amount of minutiae present on the finger. The sensors also collected the image quality of each fingerprint. This measurement is highly correlated with fingerprint scanner effectiveness and was therefore used as a representation of fingerprint readability in the experiment. A best subset test was run between the aforementioned factors and image quality in Minitab. This function tests all of the possible linear models that could be created by combining the factors against image quality and gives 2 results. The 1st result are the determined best models and the second are the statistics that tell the user how effective the models are. A model using all of the factors except pigmentation was used as the best model. However, this model only had an R2 value of 2.4, which meant that the model could only explains 2.4% of the image quality data. This provides strong evidence that there is no linear relationship between the factors and fingerprint image quality, and therefore fingerprint scanner effectiveness. In order to address the possibility of a nonlinear relationship between the factors and image quality, each factor was plotted on a graph against image quality. If the variable had a nonlinear relationship with image quality, a pattern would appear on the graph. No convincing pattern appeared on any of the graphs, which gave evidence that there is also no nonlinear relationship between the finger factors and image quality. This, combined with the previous finding concerning linear relationships, allows us to state that there is strong evidence that the factors do not correlate with fingerprint scanner effectiveness.
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