{"title":"Evidence of correlation between fingerprint quality and skin attributes","authors":"R. Hancock, S. Elliott","doi":"10.1109/CCST.2016.7815708","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":6510,"journal":{"name":"2016 IEEE International Carnahan Conference on Security Technology (ICCST)","volume":"16 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Carnahan Conference on Security Technology (ICCST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCST.2016.7815708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.