(2008) Impact of Gender on Fingerprint Recognition Systems
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(2008) Impact of Gender on Fingerprint Recognition Systems

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Fingerprint recognition is an important...

Fingerprint recognition is an important
biometric technology, and its use is increasing day by day.
Fingerprint recognition is affected by several physiological
factors like age, wear and tear of skin, and technological
factors like sensor technologies. This paper builds on
previous research in the area of gender differences in
fingerprint features, and reports results of differences in
performance of fingerprints collected from males and
females. The researchers propose a fingerprint analysis
framework for testing differences in gender and apply the
framework to fingerprints collected from males and
females.

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(2008) Impact of Gender on Fingerprint Recognition Systems (2008) Impact of Gender on Fingerprint Recognition Systems Document Transcript

  • 5th International Conference on Information Technology and Applications (ICITA 2008) Impact of Gender on Fingerprint Recognition Systems Michael D. Frick, Shimon K. Modi, Stephen J. Elliott, Ph.D., and Eric P. Kukula, Member, IEEE Abstract— Fingerprint recognition is an important compared to the younger population. These findings have biometric technology, and its use is increasing day by day. helped devise methods which improve performance of Fingerprint recognition is affected by several physiological fingerprints collected from the elderly. The impact of gender on factors like age, wear and tear of skin, and technological fingerprint recognition has not received a lot of attention from factors like sensor technologies. This paper builds on the research community. This paper presents results from a previous research in the area of gender differences in study conducted to evaluate if any differences exist in fingerprint features, and reports results of differences in fingerprint features and fingerprint matching performance performance of fingerprints collected from males and between males and females. A testing methodology is females. The researchers propose a fingerprint analysis presented which assesses differences in minutiae count, image framework for testing differences in gender and apply the quality and performance between fingerprint datasets collected framework to fingerprints collected from males and from males and females. It should be noted that this study did females. not aim to research the difference in performance between the two sensor technologies. The two different technologies were Index Terms—fingerprint recognition, biometrics, used for purposes of creating a baseline and then measuring the gender, image quality, performance assessment. relative differences for the genders within each sensor technology. I. INTRODUCTION II. REVIEW OF LITERATURE Fingerprint matching has been used in forensics and criminalistics for over a century, and the last decade has seen an Fingerprint images are heavily affected by user interaction exponential increase in use of automated fingerprint such as the amount of force applied on the fingerprint sensor, recognition. Fingerprint recognition is an example of the users familiarity with the sensor (habituation), the types of biometrics. Biometrics is defined as the automated recognition sensors and how the user interacts with it [2]. These interaction of individuals based on physiological or behavioral issues are generally grouped into an area of research called the characteristics. Fingerprint recognition are among the most Human Biometric Sensor Interaction [2]. Another is outside of widely used biometric systems [1]. The use of fingerprint the control of the user, and is more a function of the sensor. recognition has expanded to personal authentication and When a finger is pressed on a sensor, the fingerprint image is a government-to-citizen applications as well. State welfare 2D representation of a 3D object and can therefore impact the programs which use fingerprint recognition for identification fingerprint features such as ridge flow, ridge density, and purposes and United States Visitor and Immigrant Status minutiae points. These characteristics are important to Indicator Technology (US-VISIT) are examples of large scale fingerprint feature extractors and fingerprint matchers. In government applications which use fingerprint recognition. biometrics, the aim is to present the best quality image in order Such systems have to be capable of handling fingerprints from to yield good performance. While the authors have examined a diverse range of demographics. Previous studies have shown the relationship of image quality and performance, the next that the elderly population has a higher chance of a mismatch logical step is to examine the impact of gender on a fingerprint system and to add our results to the body of literature. Previous M. Frick is a researcher in the Biometrics Standards, Performance, and research has concentrated on evaluating differences in Assurance Laboratory in the Department of Industrial Technology, Purdue University, West Lafayette, IN 47907 USA (e-mail: mfrick@purdue.edu). fingerprint features of fingerprint images and on evaluation of S. K. Modi is a researcher and Ph.D. candidate in the Biometrics Standards, biometric systems when deployed in an operational scenario. In Performance, and Assurance Laboratory in the Department of Industrial [3] sought to determine if gender had an impact on fingerprint Technology, Purdue University, West Lafayette, IN 47907 USA (e-mail: shimon @purdue.edu). ridge density. They worked under the assumption that S. J. Elliott is Director of the Biometrics Standards, Performance, and fingerprints of females tended to have a thinner epidermal ridge Assurance Laboratory and Associate Professor in the Department of Industrial detail compared those of males. The thinner detail would lead Technology, Purdue University, West Lafayette, IN 47907 USA (e-mail: elliott@purdue.edu). to females having a higher ridge density compared to males. E.P. Kukula is a researcher and Ph.D. candidate in the Biometrics Standards, The study found that the ridge density was statistically Performance, and Assurance Laboratory in the Department of Industrial significant in the differences between males and females. The Technology, Purdue University, West Lafayette, IN 47907 USA (e-mail: kukula@purdue.edu). research described in [4] followed up the research performed in [3] and applied it to evaluate the differences in loop ridge count of male and female subjects. The FBI 1984 standards of ridge ICITA2008 ISBN: 978-0-9803267-2-7 717
  • counting were used to compare the fingerprint loop ridge Table I. Sensor Specifications counts from 40 males and 40 females. The study found that Sensor Technology Resolution Interaction Image Size there were no statistically significant differences in loop ridge Type (DPI) Type (pixels) counts between genders. In [5] , the research showed that males Sensor 1 Capacitive 500 Swipe 186 X 270 have higher ridge distance between the centers of two adjacent Sensor 2 Optical 500 Touch 248 X valleys compared to females, and a study by [6] showed that 292 fingerprints of females are of significantly lower quality compared to males. [7] attempted to classify gender based on fingerprint images. They used fingerprint features like ridge count, ratio of ridge thickness to valley thickness, ridge count asymmetry, and pattern type concordance. Using Fuzzy C-Means, Linear Discriminant Analysis, and Neural Networks they achieved true classification results of 80.39%, 86.5%, and 88.5% respectively. Research conducted by [8] analyzed the effect of gender on recognition rates for iris recognition rates. Their results reported that females had a lower False Reject Rate (FRR) compared to males, but the differences in gender were not Sensor 1 – Male Sensor 2 – Male statistically significant. Their report was specific to their population, application and environment, but it indicated the need to understand the impact of gender on other biometric systems. The motivation of our paper was to understand the impact of these differences on actual performance of the fingerprint system. The authors build on previous work done in this area and analyze the differences in minutiae count, image quality scores and performance between fingerprints collected from Sensor 1 – Female Sensor 2 – Female males and females. Fig. 1. Example Fingerprint Images. III. ANALYSIS FRAMEWORK A commercially available image quality software was used V. FINGERPRINT FEATURE ANALYSIS to analyze the image quality scores and minutiae count for the fingerprints. The image quality scores range between 0-100, To analyze the results, both parametric and nonparametric where 0 is considered the lowest possible score and 100 is analysis of variance (ANOVA) methods were used, based considered the highest possible score. A commercially solely on model assumptions and the resulting diagnostics for available minutiae based matcher was used for this study. The image quality scores and number of detected minutiae. For all analysis of this study was broken into three steps: statistical tests a significance level (α) of 0.05 was used. 1. Analysis of Minutiae Count between genders. 2. Analysis of Image Quality scores between genders. A. Image Quality 3. Analysis of Performance rates between genders. The image quality scores were analyzed using nonparametric methods because the data did not meet the parametric ANOVA IV. DATA COLLECTION model assumptions. Thus, the nonparametric Kruskal-Wallis The dataset used in this research was collected from one (H) test was used to analyze the quality scores for both optical optical and one capacitive swipe sensor both of which were and capacitance swipe based technologies. The results of the commercially available. Fingerprints were collected from 244 nonparametric test for the image quality scores from the optical subjects using these two different sensors. Each subject sensor revealed a statistically significant difference among the provided 3 fingerprint images from the right index finger, right median image quality scores for males and females, H(.95, 2) = middle finger, left index finger, and left middle finger. Table I 156.50, resulting in a p-value less than 0.05. To illustrate this has a specifications overview for the fingerprint sensors used in difference in image quality data by gender, overlapping the study. histograms were constructed as shown in Fig. 2. The resultant images were used in their raw format for all analysis. Fig. 1 shows sample images collected from the different sensors. 718
  • Table I. Descriptive statistics for the image quality scores by sensor technology and gender. Sensor Gender N µ x̃ σ Optical Male 1499 71.83 76.0 15.06 Female 1428 63.08 70.0 20.44 Capacitance Male 1348 71.31 76.0 16.04 Female 1239 59.74 66.0 22.19 B. Number of Detected Minutiae The number of minutiae detected from a fingerprint image can vary according to the sensing technology due to factors such as sensor area size and if motion is required. This was a particularly interesting analysis as this paper is comparing a large area optical sensor and a capacitance swipe sensor. Since Fig. 2. Optical fingerprint sensor histogram of image quality the distributions were normal, parametric ANOVA tests were scores by gender. performed to determine whether the average minutiae count for each sensing technology was statistically significant by gender. Similar results were seen for the image quality analysis for For the optical based sensor, a p-value of 0.926 was the capacitive swipe sensor. The Kruskal-Wallis test revealed a observed, which indicated that the minutiae counts for males statistically significant difference among the median image and females were not statistically different, which is shown quality scores for males and females, H(.95, 2) = 233.93, resulting visually in Fig. 4. in a p-value less than 0.05. Again to illustrate this difference in image quality data by gender, overlapping histograms were constructed for the capacitive swipe sensor, shown in Fig. 3. Fig. 4. Optical sensor histogram of number of detected minutiae by gender. Contrary to the optical analysis for number of detected Fig. 3. Capacitance swipe sensor histogram of image quality minutiae, the capacitance swipe sensor was statistically scores by gender. significant, F(.05,1,2585) = 11.94, with a p-value less than 0.05, meaning that there was a difference in the mean number of Interesting trends were also seen in the descriptive statistics minutiae points in male and female fingerprint images collected for the optical and capacitance swipe image quality scores, with the capacitance swipe sensor. This is visually seen in the which is shown in Table I. Specifically, the mean, median, overlapping histogram in Fig. 5. median, and standard deviation are very similar for the males Further examining the descriptive statistics, which is shown and females, respectively, across the two sensing technologies. in Table II for the number of detected minutiae points by gender and sensing technology the means and standard deviations are quite similar, but statistical analysis revealed differences only for the capacitance swipe sensor. 719
  • the two types of technologies. The capacitance sensor was swipe-based while the optical sensor used was a touch sensor which could have an influence in the ease of interaction, and subsequently on the type of images acquired by the sensor. For both of the sensors, the female fingerprint dataset performed better than the male fingerprint dataset, and this difference could be attributed to the finer detail of the ridge lines seen on fingerprints from females. The finer details of the ridge lines minimize the possibility of spurious features due to elastic deformation of the fingerprint when placed on the sensor. Although the DET curves showed a difference in performance, the impact of these differences on a large scale system needs further research. Fig. 5. Capacitance swipe sensor histogram of number of detected minutiae by gender. Table II. Descriptive statistics for number of detected minutiae by sensor type and gender. Optical Capacitance Gender N µ σ N µ σ Male 1499 32.53 10.22 1348 34.11 10.52 Female 1428 32.49 11.26 1239 32.66 10.91 VI. PERFORMANCE RATES ANALYSIS Performance analysis of the four datasets was performed using a commercially available minutiae based matcher. Evaluation of performance can be done using several different Fig. 6. DET curves for Optical Sensor methodologies; the researchers chose to use Detection Error Tradeoff (DET) curves for analyzing performance of the datasets. A DET curve is a modified form of a Receiver Operating Characteristic (ROC) curve, which plots the False Reject Rate (FRR) on the y-axis and False Accept Rate (FAR) on the x-axis for every possible threshold value. By overlaying the DET curves for the male and female datasets, their performance and relative differences can be assessed at every possible threshold. DET (T) = (FAR (T), FRR (T)), (1) where T is the threshold Fig. 6 shows the DET curves for male and female fingerprint dataset collected on the optical sensor. The DET curves show that the difference in performance between the two datasets is not significantly different. Fig. 7 shows the DET curves for male and female dataset collected on the capacitive sensor. The DET curves show that a difference in performance exists between the male and female gender, but only for FAR of less Fig. 7. DET curves for Capacitive Sensor than 0.1%. For FAR of more than 0.1%, the performance of the two datasets is similar. The DET curves show that the relative difference in performance between males and females is different between 720
  • The image quality of males was found to be higher compared REFERENCES to the image quality of females, which did lead to better [1] IBG, Biometrics Market and Industry Report. 2007, IBG: NY. p. performance for fingerprints collected from males compared to 224. females. Although a difference existed in the image quality [2] Elliott, S.J., E. Kukula, and S. Modi, Issues Involving the Human Biometric Sensor Interface, in Image Pattern Recognition Synthesis scores, both genders exhibited fingerprints of high quality and Analysis in Biometrics, S. Yanushkevich, P. Wang, and S. scores. Also the minutiae count for fingerprints from males and Srihari, Editors. 2007, World Scientific Publishers. p. 400. females was relatively similar. Even though image quality has [3] Acree, M., Is there a gender difference in fingerprint ridge density? an impact on performance, the significance of the impact is Forensic Science International, 1999. 102(1): p. 35-44. [4] Bell, A., Loop ridge count differences between genders. 2006, noticed only when a large difference in image quality scores is Nebraska Wesleyan University. p. 13. observed. The results show that further work is required in [5] Kralik, M. and V. Novotny, Epidermal ridge breadth: an indicator understanding the differences in fingerprint features between of age and sex in paleodermatoglyphics. Variability and Evolution, males and females and their impact on performance. 2003. 11(5): p. 30. [6] Hicklin, R. and C. Reedy, Implications of the IDENT/IAFIS Image Quality Study for Visa Fingerprint Processing. 2002, Mitretek Systems. VII. CONCLUSIONS AND FUTURE WORK [7] Badawi, A., et al. Fingerprint-Based Gender Classification. in IPCV. 2006. Las Vegas, Nevada: CSREA Press. Previous research has shown that there are differences in [8] Mansfield, T., et al., Biometric Product Testing Final Report. 2001, ridge density and ridge loops for fingerprints from males and National Physics Laboratory: Middlesex. p. 25. females. With large scale adoption of fingerprint recognition [9] Elliott, S.J. and S.K. Modi. Impact of Image Quality on Performance: Comparison of Young and Elderly Fingerprints. in for civilian applications, the knowledge of gender differences 6th International Conference on Recent Advances in Soft on performance of fingerprint recognition is essential. Computing (RASC). 2006. Canterbury, UK. The results discussed in this paper highlight several areas of research which would be helpful in real world applications of fingerprint recognition. Age has been a factor which affects fingerprint recognition, which has been shown in [9]. Combining the factors of age and gender and understanding their effect on fingerprint recognition systems performance would be important to large scale implementers of this technology. Since fingerprint feature details are different between males and females, it would be interesting to create fingerprint extraction and matching modules which are customized to each gender. This would allow the extractor and matcher to take advantage of the differences in features which are unique to each gender. This study has shown a difference in performance between males and females through the use of DET curves, but the significance of differences cannot be established by the use of DET curves. A statistical basis for testing differences in performance is needed to evaluate its impact on implemented systems. Acquisition of fingerprints is affected by the ability of skin to retain moisture, its elasticity and oiliness. Evaluation of these factors in males and females could also help in establishing the reasons for differences in performance between the two genders. 721