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(2007) The impact of fingerprint force on image quality and the detection of minutiae
(2007) The impact of fingerprint force on image quality and the detection of minutiae
(2007) The impact of fingerprint force on image quality and the detection of minutiae
(2007) The impact of fingerprint force on image quality and the detection of minutiae
(2007) The impact of fingerprint force on image quality and the detection of minutiae
(2007) The impact of fingerprint force on image quality and the detection of minutiae
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(2007) The impact of fingerprint force on image quality and the detection of minutiae

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  • 1. IEEE EIT 2007 Proceedings 432 . The Impact of Fingerprint Force on Image Quality and the Detection of Minutiae Eric Kukula, Stephen Elliott, Hakil Kim, and Cristina San Martin Of primary importance during selection of an authentication Abstract—It is well documented that many factors affect system and whether or not to implement a biometric system is fingerprint image quality such as age, ethnicity, moisture, to first understand how the target population will react to temperature and force, although force has only been subjectively biometric technologies, determine what issues might arise, and measured in the literature. Fingerprint image quality is of utmost importance due to its linear relationship with matching understand who your users are and their knowledge, performance. Therefore, the purpose of this research is to show perception, and anxiousness with using technology. Other how fingerprint force impacts image quality and the number of questions to consider include how factors such as temperature, detected minutiae. Two experiments are presented in this paper illumination, noise, etc… affect the performance of the that evaluated fingerprint force levels and the impacts on image biometric system. quality, number of minutiae detected, and user comfort to provide Understanding the design of the authentication system and the community with a quantitative measure for force as it relates to image quality. Four force levels (3, 9, 15, and 21 newtons) were the biometric sub-system (and the interaction of the individual evaluated in the first experiment with results indicating that there and the biometric sensor) is critical, as it must accommodate as was no incremental benefit in terms of image quality when using many of the intended users as possible, and work in the targeted more than 9N when interacting with an optical fingerprint sensor. environment. Once these factors have been taken into The second experiment investigated the 3-9N interval with results consideration, the last step in system evaluation is to evaluate indicating that the optimal image quality is arrived between a whether the biometric systems expected performance will force level is 5-7N. ultimately satisfy the intended purpose for not only the Index Terms— fingerprint, image quality, biometrics application, but also the users. According to Jain, Pankanti, et al., the complexity of designing a biometric system is based on I. INTRODUCTION three main attributes – accuracy, scale (size of the database), and usability [2]. As utilization of biometric technology B IOMETRIC technology is defined as the automated recognition of behavioral and physiological characteristics of an individual [1]. When deciding whether to implement an becomes more pervasive, understanding the interaction between the human, the environment, and the biometric sensor becomes increasingly imperative. authentication system, many considerations have to be Fingerprint recognition is used in a number of wide ranging examined in order to assess whether incorporating biometric applications including law enforcement (AFIS), access control, technologies is suitable. For example, should an access control time and attendance, and logical access. Fingerprint recognition system utilize a magnetic lock, a touchpad, or a biometric? A has an extensive history, but it was not until the late nineteenth non-exhaustive list of factors that would likely influence this century that the modern fingerprint classification system was decision includes the intended users, the environment, the proposed by Francis Galton and Edward Henry, first as application, and the design of the system/device. The success of independent classifications and subsequently as a singular, biometric technology relies closely on the sensors ability to comprehensive system. Galton’s contribution to the collect and extract those characteristics from a vast pool of comprehensive classification system focused on minutiae individuals. points, or singular points of interest that are caused by a change in the ridge of the fingerprint. Two of the more common types of minutiae are ridge endings and ridge bifurcations, or forks. Eric P. Kukula is a graduate researcher in the Biometrics Standards, Part of the definition of biometric systems is that they are Performance, & Assurance Laboratory, in the Department of Industrial Technology, Purdue University, 401 N. Grant Street, West Lafayette, Indiana automatic; with regard to fingerprint identification, users 47907.USA (phone: 765-494-1101; fax: 765-496-2700; e-mail: kukula@ present their fingerprints to a sensor. Of the five common purdue.edu). URL: http://www.biotown.purdue.edu/research/ergonomics.asp. families of fingerprint sensors (optical, capacitance, thermal, Stephen J. Elliott is Director of the Biometrics Standards, Performance, & Assurance Laboratory and Associate Professor in the Department of Industrial ultrasound, and touchless), the two most widely used are optical Technology, Purdue University, 401 N. Grant Street, West Lafayette, Indiana and capacitance. Optical sensors are more commonly used in 47907.USA (e-mail: elliott@ purdue.edu). law enforcement, border control, and desktop authentication Hakil Kim is a Professor in the School of Information & Communication applications, whereas capacitance sensors are found in laptops, Engineering at Inha University and a member of Biometrics Engineering Research Center (BERC) at Yonsei University, 253 Yonghyun-dong, Nam-gu, cellular phones, personal data assistants (PDAs), and flash Incheon, Korea 402-751 (e-mail: hikim@ inha.ac.kr). drives. There is some degree of overlap between capacitance Cristina San Martin is a graduate student in the Department of Computer and sensors and optical sensors, particularly in access control and Information Technology, Purdue University, 401 N. Grant Street, West Lafayette, Indiana 47907.USA (e-mail: csanmart@ purdue.edu). desktop security applications. Fingerprints are matched either 1-4244-0941-1/07/$25.00 c 2007 IEEE Authorized licensed use limited to: Purdue University. Downloaded on February 27,2010 at 11:03:27 EST from IEEE Xplore. Restrictions apply.
  • 2. IEEE EIT 2007 Proceedings 433 . through pattern or minutiae extraction. The minutiae-based III. METHODOLOGY method is typically used by optical sensors (due to the image Two experiments were performed with a CrossMatch size), whereas the pattern-based method has been developed for VerifierTM 300 LC single optical fingerprint capture device to the smaller area sensors found on consumer devices. measure the impact fingerprint force has on image quality. The sensor had the following properties: resolution – 500 DPI ±1%, II. BACKGROUND image size (pixels) – 640*480, platen size (inches) – 1.2*1.2, A. Motivation for this Research and operating temperature – 0°F to 104°F. This sensor was The motivation for this research was to determine if the force chosen due to its widespread deployment in over 5,000 (pressure) an individual applies to an optical fingerprint sensor applications, some of which include: national ID/registration can be correlated with the resulting image quality. Kang, et al. programs, border and port entry/exit control, and Child ID examined finger force and indicated force does impact quality, programs [11]. To measure the force placed on the fingerprint but did not specify quantitative measures, rather classified force sensor a Vernier Dual-Range Force Sensor was used. The force as low (softly pressing), middle (normally pressing), and high sensor had a range of ±50N and error of ±0.05N. Fig. 1 shows (strongly pressing) [3]. Thus, the purpose of this research is to the experimental setup. quantitatively analyze the impact of fingerprint pressing force on both image quality and the number of detected minutiae on fingerprint image quality. This is of importance as image quality effects the biometric matching algorithm as discussed in [4-7]. B. Influence of the Scientific Discipline of Human Factors and Human-Computer Interaction on Biometrics Historically the biometrics community has performed limited work in the area of human-computer interaction and related fields of ergonomics and usability. Recent work conducted by the National Institute of Standards and Fig. 1: Experimental setup showing the optical fingerprint sensor and force Technology (NIST) examined the impact different heights of a sensor. fingerprint sensor have on image quality and capture time. The study also examined user preferences to particular heights [8]. Both experimental procedures required participants to use Results from the study consisting of 75 NIST employees their right index finger to reduce some variability in revealed a counter height of 36 inches (914 mm) gives fastest measurement in terms of dexterity and finger size, but could not performance, while the 26 inch (660 mm) counter height gave account for all variability between people. In cases of extreme the highest quality fingerprint images, and a counter height of scarring or other irregularities, the left index finger or middle 32 or 36 inches (813 or 914 mm) was the most comfortable for fingers were used. Three fingerprint images were collected and users [8]. stored for each force level used. Fingerprint capture was Similar work has been conducted by Kukula, Elliott, et. al performed by the test administrator, which grabbed the with hand geometry to investigate the effect that different fingerprint image when the force level (f) was within the set working heights have on the performance of a hand geometry tolerance of f ± 0.50N for the first experiment and f ± 0.25N for device [9]. Four hand geometry devices were used in this the second. The precision was increased for the second evaluation with the heights at which devices were placed at the experiment due to the measured force increments reducing three recommended height ranges provided by Grandjean for from 6N to 2N. Experiment 1 used four force levels: 3N, 9N, the different types of work: precision, light, and heavy [10]. 15N, and 21N, where as experiment 2 used five force levels: The fourth device was mounted at a similar height as the hand 3N, 5N, 7N, 9N, and 11N. The order of image collection geometry device that was found on the Purdue BSPA followed the same procedure for all participants which went in laboratory door. The results of the 32 participant evaluation increasing order of force levels. Since image quality was the revealed that there was no difference among the median value dependent variable, the platen was cleaned with a micro fiber of match scores across the different four heights (30, 35, 40, towel between each finger placement to ensure sweat and oil and 45 inches), thus allowing the biometric device installer residue was not on the platen. After the completion of each some flexibility when implementing hand geometry devices. force level, the participants answered a usability question based Users were also surveyed to establish which device satisfied on comfort levels, which are shown in Fig. 2. Data collection them. The results showed that 63% of the users preferred the was performed with all subjects seated on a stool at a height hand geometry device mounted at 40 inches. In addition, the above the sensor, to minimize stressors on the body. study revealed a correlation of preferred hand geometry height and that of the user; therefore a practitioner should take into consideration the height of the intended user audience before installing a hand geometry device. Authorized licensed use limited to: Purdue University. Downloaded on February 27,2010 at 11:03:27 EST from IEEE Xplore. Restrictions apply.
  • 3. IEEE EIT 2007 Proceedings 434 . B. Within Experiment Analysis " !!!# !!!$ =1 =2 =3 To analyze the results of each experiment multiple Analysis CIRCLE THE FACE THAT BEST DESCRIBES of Variance (ANOVA) tests were performed. ANOVA tests are YOUR COMFORT AT THIS LEVEL an instrument to compare the effect of multiple levels of one factor (force) on a response variable (image quality, number of minutiae), which is a generalization of the two-sample t-test. level.! Fig. 2: Usability metric of user comfort asked after interaction at each force The ANOVA is partitioned into two segments: the variation that is explained by the model (2) and the variation not Once the fingerprint samples were collected, the prints were explained, or error (3), which are both used to calculate the analyzed with Aware Wavelet Scalar Quantization (WSQ) F-statistic (4) testing the hypothesis Ho: 1 = 2 = … = I and VBQuality software v2.42E. The following variables were Ha: not all ’s are the same. In practice p values are used, but reported by the software: quality score, minutiae, and the the Fobserved test statistic can also be compared to the F number of core(s)/delta(s). The image quality score ranged distribution table as shown in (5). Typically when the Ho is from 0-99, with zero being a bad quality image score and 99 rejected the variation of the model (SSM) tends to be larger being the best quality score. than the error (SSE), which corresponds to a larger F value. IV. EXPERIMENTAL DESIGN ( ) SSM = ' Yi − Y , dfM = 1, MSM = SSM dfM ˆ 2 (2) SSE = ' (Y − Y ) , dfE = n − 2, MSE = SSE dfE A. Between Experiment Analysis 2 In order to compare results from the two experiments, the ˆ i i (3) experimental design was created in such a way that two control F = MSM MSE ~ F (dfM , dfE ) (4) groups (force levels of 3N and 9N) were the same in both experiments, except for the reduced tolerance difference in F ≥ F (1 − α , dfM , dfE ) (5) experiment two. Note the graphs for 3N and 9N are colored differently in Figs. 3 – 6 for ease in comparing. Two-sample V. EXPERIMENT ONE t-tests were performed on the 3N and 9N image quality scores and minutiae counts to examine if there were differences A. Specific Procedures between the two studies. The equation for the two-sample t-test Experiment 1 consisted of 29 participants between the age of can be found in (1), with hypothesis Ho: µ1 = µ2 and Ha µ1 ! µ2. 18 and 25 which took place in October of 2006. In this study, 2 individuals had scarring or other irregularities, of which one used the right middle, and one used the left index finger. Four & s12 # & s 2 2 # T = (Y 1 − Y 2 ) $ N !+$ N ! (1) force levels were evaluated: 3N, 9N, 15N, and 21N. Fingerprint % 1" % 2" images for each of the corresponding force levels can be seen in Fig. 3. The two sample t-test of image quality score by experiment for force level 3N revealed that there were no differences in the means, t (0.975, 182) = -1.37, p = 0.171. The 9N two-sample t-test revealed there were also no significant differences in the means of image quality scores between the two experiments, t(0.975, 192) = 0.83, p = 0.406. While the image quality scores were similar between the two 3N Force 9N Force 15N Force 21N Force Quality 53 Quality 60 Quality 74 Quality 84 experiments, the numbers of minutiae between the two experiments at the 3N and 9N force levels were statistically Fig. 3: Fingerprint images for Experiment One by force level with reported significant, t(0.975, 197) = -3.27, p = 0.001 and t(0.975, 213) = -3.39, p image quality score. = 0.001, for 3N and 9N respectively. Thus, the results of the t-tests indicate that the two test B. Statistical Analysis and Results populations used in the two experiments are similar in terms of The results of Experiment One’s ANOVA for image quality overall image quality scores, but differ in minutiae, which may score revealed that there was a statistically significant be attributable to test crew composition (ethnicity, gender, difference between image quality scores and the four force finger moisture) and external environmental conditions levels (3N, 9N, 15N, and 21N) applied to the sensor, F(.95, 3, 344) (temperature, humidity) as discussed in [5, 7, 12, 13], but were = 22.56, p = 0.000. The frequency plots of image quality scores not evaluated in either of the experiments. can be seen in Fig. 4, which is organized by force level. This plot graphically depicts the ANOVA results – the quality scores for 3N are more spread than the other three force levels. Authorized licensed use limited to: Purdue University. Downloaded on February 27,2010 at 11:03:27 EST from IEEE Xplore. Restrictions apply.
  • 4. IEEE EIT 2007 Proceedings 435 . Fig. 6: Experiment one user reported comfort level by force level. Fig. 4: Experiment one frequency plot of quality scores by force level. VI. EXPERIMENT TWO Investigating the quality scores further, one can reach the Similar to image quality, the number of minutiae located was conclusion that the scores significantly increased between 3N more spread at the lower end of the distribution for the 3N force and 9N, but there was minimal benefit of applying more than level than the other three levels (Fig. 5). The ANOVA results 9N of force, as the quality scores only increased minimally. confirm this, as there was a statistically significant difference Thus, we investigated the 3N – 9N interval in experiment two. between number of minutiae and force level, F (.95, 3, 344) = 30.69, A. Specific Procedures p = 0.000. Experiment 2 consisted of 43 participants aged between 18-25 years old and took place in January of 2007. The subjects were unique to each test. All participants used their right index finger. The five force levels investigated were: 3N, 5N, 7N, 9N, and 11N. Fingerprint images for one user at each of the corresponding force levels can be seen in Fig. 7. 3N Force 5N Force 7N Force 9N Force 11N Force Quality 3 Quality 87 Quality 91 Quality 88 Quality 90 Fig. 7: Fingerprint images for Experiment Two by force level with reported image quality score. Fig. 5: Experiment one frequency plot of minutiae count by force level. B. Statistical Analysis and Results The results of Experiment Two’s ANOVA for image quality score revealed that there was a statistically significant The subjective comfort level question also revealed that the difference between image quality scores and the five force more pressure a user applied, the more uncomfortable it is for levels (3N, 5N, 7N, 9N, and 11N) applied to the sensor, F(.95, 4, subjects to interact with the device (Fig. 6). 640) = 6.88, p = 0.000. However, as expected the value of the F statistic is lower for this model, which is attributable to the smaller force level increments (2N between levels as opposed to 6N in Experiment 1) under investigation. The frequency plots of image quality scores can be observed in Fig. 8, which is organized by force level. This plot graphically depicts the ANOVA results – the quality scores for 3N are more spread than the other three force levels which are heavily skewed to the left. Authorized licensed use limited to: Purdue University. Downloaded on February 27,2010 at 11:03:27 EST from IEEE Xplore. Restrictions apply.
  • 5. IEEE EIT 2007 Proceedings 436 . Fig. 10: Experiment two user reported comfort level by force level. VII. CONCLUSION The purpose of this study is to quantitatively analyze the impact of fingerprint pressing force on both image quality and the number of detected minutiae. Investigating the quality scores of experiment two further, one can deduce that the Fig. 8: Experiment two frequency plot of quality scores by force level. quality scores significantly increase between the 3N and 5N-7N Since the force level intervals were smaller, there was more force level, and actually regressed at 11N, but there was of an overlap in the number of minutiae located; however, the minimal benefit of applying more than 9N of force, as the number was denser at lower end of the distribution for the 3N quality scores did not improve by much, plus were deemed as force level than the other four levels (Fig. 9). The ANOVA neutral or unsatisfactory by the users. Moreover, it is apparent results revealed a statistically significant difference between from these two experiments, the users were less comfortable number of minutiae and force level, F(.95, 4, 640) = 19.52, p = using the fingerprint device when more force had to be applied 0.000, which like the image quality for experiment two, the F to the sensor with their finger. Thus, the recommended force statistic was lower than for experiment one due to the smaller level an individual should apply to an optical sensor such as the intervals. one used in these experiments should be approximately five to seven newtons, which is approximately two newtons more than what an average person applies when typing on a computer keyboard (3-5 newtons). REFERENCES [1] International Organization for Standardization, "ISO/IEC JTC1/SC37 Standing Document 2 - Harmonized Biometric Vocabulary," WD 2.56 ed: ANSI, 2007, pp. 66. [2] A. Jain, S. Pankanti, S. Prabhakar, L. Hong, and A. Ross, "Biometrics: A Grand Challenge," presented at 17th International Conference on Pattern Recognition (ICPR 2004), Guildford, UK, 2004. [3] K. Kang, B. Lee, H. Kim, D. Shin, and J. Kim, "A Study on Performance Evaluation of Fingerprint Sensors " in Audio- and Video-Based Biometric Person Authentication, Lecture Notes in Computer Science, G. Goos, J. Hartmanis, and J. van Leeuwen, Eds. Berlin / Heidelberg: Springer 2003, pp. 574-583. [4] A. Jain, Y. Chen, and S. Dass, "Fingerprint Quality Indices for Predicting Authentication Performance," presented at 5th International Conf. on Audio- and Video-Based Biometric Person Authentication, Rye Brook, NY, 2005. Fig. 9: Experiment two frequency plot of minutiae count by force level. [5] S. K. Modi and S. J. Elliott, "Impact of Image Quality on Performance: Comparison of Young and Elderly Fingerprints," The subjective comfort level question for experiment two presented at 6th International Conference on Recent Advances in Soft Computing (RASC), Canterbury, UK, 2006. was interesting, as participants were very neutral to applying 7 [6] E. Tabassi and C. L. Wilson, "A novel approach to fingerprint image or 9 newtons of force to the sensor, where as in Experiment one quality," presented at International Conference on Image most participants were comfortable with applying 9N of force. Processing, Genoa, Italy, 2005. [7] M. Yao, S. Pankanti, and N. Haas, "Fingerprint Quality Overall, the frequencies for experiment two (Fig. 10) correlate Assessment," in Automatic Fingerprint Recognition Systems, N. with those of experiment one. Ratha and R. Bolle, Eds. New York: Springer, 2004, pp. 55-66. Authorized licensed use limited to: Purdue University. Downloaded on February 27,2010 at 11:03:27 EST from IEEE Xplore. Restrictions apply.
  • 6. IEEE EIT 2007 Proceedings 437 . [8] M. Theofanos, S. Orandi, R. Micheals, B. Stanton, and N. Zhang, "Effects of Scanner Height on Fingerprint Capture," National Institute of Standards and Technology, Gaithersburg NISTIR 7382, December 14, 2006. [9] E. Kukula, S. Elliott, P. Senarith, and S. Tamer, "Biometrics and Manufacturing: A Recommendation of Working Height to Optimize Performance of a Hand Geometry Machine," Purdue University Biometrics Standards, Performance, & Assurance Laboratory, 2007, pp. 18. [10] E. Grandjean, Fitting the Task to the Man: A Textbook of Occupational Ergonomics, 4 ed. London: Taylor & Francis, 1988. [11] CrossMatch Technologies, "Verifier 300 LC 2.0 Single Finger Scanner with USB 2.0 Interface," Palm Beach Gardens n.d. [12] International Organization for Standardization, "ISO/IEC JTC1/SC37 - Information Technology – Biometric Performance Testing and Reporting – Part 3: Technical Report on Modality-Specific Testing ": ANSI, 2007, pp. 27. [13] N. Sickler and S. Elliott, "An evaluation of fingerprint image quality across an elderly population vis-a-vis an 18-25 year old population," presented at 39th Annual International Carnahan Conference on Security Technology (ICCST), Las Palmas de Gran Canaria, Spain, 2005. Authorized licensed use limited to: Purdue University. Downloaded on February 27,2010 at 11:03:27 EST from IEEE Xplore. Restrictions apply.

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