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

IEEE EIT 2007 Proceedings 432.

1-4244-0941-1/07/$25.00 c©2007 IEEE

Abstract—It is well documented that many factors affect

fingerprint image quality such as age, ethnicity, moisture, temperature and force, although force has only been subjectively measured in the literature. Fingerprint image quality is of utmost importance due to its linear relationship with matching performance. Therefore, the purpose of this research is to show how fingerprint force impacts image quality and the number of detected minutiae. Two experiments are presented in this paper that evaluated fingerprint force levels and the impacts on image quality, number of minutiae detected, and user comfort to provide the community with a quantitative measure for force as it relates to image quality. Four force levels (3, 9, 15, and 21 newtons) were evaluated in the first experiment with results indicating that there was no incremental benefit in terms of image quality when using more than 9N when interacting with an optical fingerprint sensor. The second experiment investigated the 3-9N interval with results indicating that the optimal image quality is arrived between a force level is 5-7N.

Index Terms— fingerprint, image quality, biometrics

I. INTRODUCTION IOMETRIC technology is defined as the automated recognition of behavioral and physiological characteristics

of an individual [1]. When deciding whether to implement an authentication system, many considerations have to be examined in order to assess whether incorporating biometric technologies is suitable. For example, should an access control system utilize a magnetic lock, a touchpad, or a biometric? A non-exhaustive list of factors that would likely influence this decision includes the intended users, the environment, the application, and the design of the system/device. The success of biometric technology relies closely on the sensors ability to collect and extract those characteristics from a vast pool of individuals.

Eric P. Kukula is a graduate researcher in the Biometrics Standards,

Performance, & Assurance Laboratory, in the Department of Industrial Technology, Purdue University, 401 N. Grant Street, West Lafayette, Indiana 47907.USA (phone: 765-494-1101; fax: 765-496-2700; e-mail: kukula@ purdue.edu). URL: http://www.biotown.purdue.edu/research/ergonomics.asp.

Stephen J. Elliott is Director of the Biometrics Standards, Performance, & Assurance Laboratory and Associate Professor in the Department of Industrial Technology, Purdue University, 401 N. Grant Street, West Lafayette, Indiana 47907.USA (e-mail: elliott@ purdue.edu).

Hakil Kim is a Professor in the School of Information & Communication Engineering at Inha University and a member of Biometrics Engineering Research Center (BERC) at Yonsei University, 253 Yonghyun-dong, Nam-gu, Incheon, Korea 402-751 (e-mail: hikim@ inha.ac.kr).

Cristina San Martin is a graduate student in the Department of Computer and Information Technology, Purdue University, 401 N. Grant Street, West Lafayette, Indiana 47907.USA (e-mail: csanmart@ purdue.edu).

Of primary importance during selection of an authentication system and whether or not to implement a biometric system is to first understand how the target population will react to biometric technologies, determine what issues might arise, and understand who your users are and their knowledge, perception, and anxiousness with using technology. Other questions to consider include how factors such as temperature, illumination, noise, etc… affect the performance of the biometric system.

Understanding the design of the authentication system and the biometric sub-system (and the interaction of the individual and the biometric sensor) is critical, as it must accommodate as many of the intended users as possible, and work in the targeted environment. Once these factors have been taken into consideration, the last step in system evaluation is to evaluate whether the biometric systems expected performance will ultimately satisfy the intended purpose for not only the application, but also the users. According to Jain, Pankanti, et al., the complexity of designing a biometric system is based on three main attributes – accuracy, scale (size of the database), and usability [2]. As utilization of biometric technology becomes more pervasive, understanding the interaction between the human, the environment, and the biometric sensor becomes increasingly imperative.

Fingerprint recognition is used in a number of wide ranging applications including law enforcement (AFIS), access control, time and attendance, and logical access. Fingerprint recognition has an extensive history, but it was not until the late nineteenth century that the modern fingerprint classification system was proposed by Francis Galton and Edward Henry, first as independent classifications and subsequently as a singular, comprehensive system. Galton’s contribution to the comprehensive classification system focused on minutiae 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.

Part of the definition of biometric systems is that they are automatic; with regard to fingerprint identification, users present their fingerprints to a sensor. Of the five common families of fingerprint sensors (optical, capacitance, thermal, ultrasound, and touchless), the two most widely used are optical and capacitance. Optical sensors are more commonly used in law enforcement, border control, and desktop authentication applications, whereas capacitance sensors are found in laptops, cellular phones, personal data assistants (PDAs), and flash drives. There is some degree of overlap between capacitance sensors and optical sensors, particularly in access control and desktop security applications. Fingerprints are matched either

The Impact of Fingerprint Force on Image Quality and the Detection of Minutiae

Eric Kukula, Stephen Elliott, Hakil Kim, and Cristina San Martin

B

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through pattern or minutiae extraction. The minutiae-based method is typically used by optical sensors (due to the image size), whereas the pattern-based method has been developed for the smaller area sensors found on consumer devices.

II. BACKGROUND

A. Motivation for this Research The motivation for this research was to determine if the force

(pressure) an individual applies to an optical fingerprint sensor can be correlated with the resulting image quality. Kang, et al. examined finger force and indicated force does impact quality, but did not specify quantitative measures, rather classified force as low (softly pressing), middle (normally pressing), and high (strongly pressing) [3]. Thus, the purpose of this research is to 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 Technology (NIST) examined the impact different heights of a fingerprint sensor have on image quality and capture time. The study also examined user preferences to particular heights [8].

Results from the study consisting of 75 NIST employees revealed a counter height of 36 inches (914 mm) gives fastest performance, while the 26 inch (660 mm) counter height gave the highest quality fingerprint images, and a counter height of 32 or 36 inches (813 or 914 mm) was the most comfortable for users [8].

Similar work has been conducted by Kukula, Elliott, et. al with hand geometry to investigate the effect that different working heights have on the performance of a hand geometry device [9]. Four hand geometry devices were used in this evaluation with the heights at which devices were placed at the three recommended height ranges provided by Grandjean for the different types of work: precision, light, and heavy [10]. The fourth device was mounted at a similar height as the hand geometry device that was found on the Purdue BSPA laboratory door. The results of the 32 participant evaluation revealed that there was no difference among the median value of match scores across the different four heights (30, 35, 40, and 45 inches), thus allowing the biometric device installer some flexibility when implementing hand geometry devices. Users were also surveyed to establish which device satisfied them. The results showed that 63% of the users preferred the hand geometry device mounted at 40 inches. In addition, the 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.

III. METHODOLOGY Two experiments were performed with a CrossMatch

VerifierTM 300 LC single optical fingerprint capture device to measure the impact fingerprint force has on image quality. The sensor had the following properties: resolution – 500 DPI ±1%, image size (pixels) – 640*480, platen size (inches) – 1.2*1.2, and operating temperature – 0°F to 104°F. This sensor was chosen due to its widespread deployment in over 5,000 applications, some of which include: national ID/registration programs, border and port entry/exit control, and Child ID programs [11]. To measure the force placed on the fingerprint sensor a Vernier Dual-Range Force Sensor was used. The force sensor had a range of ±50N and error of ±0.05N. Fig. 1 shows the experimental setup.

Fig. 1: Experimental setup showing the optical fingerprint sensor and force sensor.

Both experimental procedures required participants to use

their right index finger to reduce some variability in measurement in terms of dexterity and finger size, but could not account for all variability between people. In cases of extreme scarring or other irregularities, the left index finger or middle fingers were used. Three fingerprint images were collected and stored for each force level used. Fingerprint capture was performed by the test administrator, which grabbed the fingerprint image when the force level (f) was within the set tolerance of f ± 0.50N for the first experiment and f ± 0.25N for the second. The precision was increased for the second experiment due to the measured force increments reducing from 6N to 2N. Experiment 1 used four force levels: 3N, 9N, 15N, and 21N, where as experiment 2 used five force levels: 3N, 5N, 7N, 9N, and 11N. The order of image collection followed the same procedure for all participants which went in increasing order of force levels. Since image quality was the dependent variable, the platen was cleaned with a micro fiber towel between each finger placement to ensure sweat and oil residue was not on the platen. After the completion of each force level, the participants answered a usability question based on comfort levels, which are shown in Fig. 2. Data collection was performed with all subjects seated on a stool at a height above the sensor, to minimize stressors on the body.



Fig. 2: Usability metric of user comfort asked after interaction at each force level.!

Once the fingerprint samples were collected, the prints were

analyzed with Aware Wavelet Scalar Quantization (WSQ) VBQuality software v2.42E. The following variables were reported by the software: quality score, minutiae, and the number of core(s)/delta(s). The image quality score ranged from 0-99, with zero being a bad quality image score and 99 being the best quality score.

IV. EXPERIMENTAL DESIGN

A. Between Experiment Analysis In order to compare results from the two experiments, the

experimental design was created in such a way that two control groups (force levels of 3N and 9N) were the same in both experiments, except for the reduced tolerance difference in experiment two. Note the graphs for 3N and 9N are colored differently in Figs. 3 – 6 for ease in comparing. Two-sample t-tests were performed on the 3N and 9N image quality scores and minutiae counts to examine if there were differences between the two studies. The equation for the two-sample t-test can be found in (1), with hypothesis Ho: µ1 = µ2 and Ha µ1 ! µ2.

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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 experiments, the numbers of minutiae between the two experiments at the 3N and 9N force levels were statistically significant, t(0.975, 197) = -3.27, p = 0.001 and t(0.975, 213) = -3.39, p = 0.001, for 3N and 9N respectively.

Thus, the results of the t-tests indicate that the two test populations used in the two experiments are similar in terms of overall image quality scores, but differ in minutiae, which may be attributable to test crew composition (ethnicity, gender, finger moisture) and external environmental conditions (temperature, humidity) as discussed in [5, 7, 12, 13], but were not evaluated in either of the experiments.

B. Within Experiment Analysis To analyze the results of each experiment multiple Analysis

of Variance (ANOVA) tests were performed. ANOVA tests are 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. The ANOVA is partitioned into two segments: the variation that is explained by the model (2) and the variation not explained, or error (3), which are both used to calculate the F-statistic (4) testing the hypothesis Ho: 1 = 2 = … = I and Ha: not all ’s are the same. In practice p values are used, but the Fobserved test statistic can also be compared to the F distribution table as shown in (5). Typically when the Ho is rejected the variation of the model (SSM) tends to be larger than the error (SSE), which corresponds to a larger F value.

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V. EXPERIMENT ONE

A. Specific Procedures Experiment 1 consisted of 29 participants between the age of

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 force levels were evaluated: 3N, 9N, 15N, and 21N. Fingerprint images for each of the corresponding force levels can be seen in Fig. 3.

Fig. 3: Fingerprint images for Experiment One by force level with reported image quality score.

B. Statistical Analysis and Results The results of Experiment One’s ANOVA for image quality

score revealed that there was a statistically significant difference between image quality scores and the four force levels (3N, 9N, 15N, and 21N) applied to the sensor, F(.95, 3, 344) = 22.56, p = 0.000. The frequency plots of image quality scores 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.

3N Force Quality 53

9N Force Quality 60

15N Force Quality 74


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Fig. 4: Experiment one frequency plot of quality scores by force level.

Similar to image quality, the number of minutiae located was more spread at the lower end of the distribution for the 3N force level than the other three levels (Fig. 5). The ANOVA results confirm this, as there was a statistically significant difference between number of minutiae and force level, F (.95, 3, 344) = 30.69, p = 0.000.

Fig. 5: Experiment one frequency plot of minutiae count by force level.

The subjective comfort level question also revealed that the more pressure a user applied, the more uncomfortable it is for subjects to interact with the device (Fig. 6).

Fig. 6: Experiment one user reported comfort level by force level.

VI. EXPERIMENT TWO Investigating the quality scores further, one can reach the

conclusion that the scores significantly increased between 3N and 9N, but there was minimal benefit of applying more than 9N of force, as the quality scores only increased minimally. Thus, we investigated the 3N – 9N interval in experiment two.

A. Specific Procedures 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.

Fig. 7: Fingerprint images for Experiment Two by force level with reported image quality score.

B. Statistical Analysis and Results The results of Experiment Two’s ANOVA for image quality

score revealed that there was a statistically significant difference between image quality scores and the five force levels (3N, 5N, 7N, 9N, and 11N) applied to the sensor, F(.95, 4,

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.

3N Force Quality 3

5N Force Quality 87

7N Force Quality 91

9N Force Quality 88




Fig. 8: Experiment two frequency plot of quality scores by force level.

Since the force level intervals were smaller, there was more of an overlap in the number of minutiae located; however, the number was denser at lower end of the distribution for the 3N force level than the other four levels (Fig. 9). The ANOVA results revealed a statistically significant difference between number of minutiae and force level, F(.95, 4, 640) = 19.52, p = 0.000, which like the image quality for experiment two, the F statistic was lower than for experiment one due to the smaller intervals.

Fig. 9: Experiment two frequency plot of minutiae count by force level.

The subjective comfort level question for experiment two was interesting, as participants were very neutral to applying 7 or 9 newtons of force to the sensor, where as in Experiment one most participants were comfortable with applying 9N of force. Overall, the frequencies for experiment two (Fig. 10) correlate with those of experiment one.

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 quality scores significantly increase between the 3N and 5N-7N force level, and actually regressed at 11N, but there was minimal benefit of applying more than 9N of force, as the quality scores did not improve by much, plus were deemed as neutral or unsatisfactory by the users. Moreover, it is apparent from these two experiments, the users were less comfortable using the fingerprint device when more force had to be applied to the sensor with their finger. Thus, the recommended force level an individual should apply to an optical sensor such as the 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.

[5] S. K. Modi and S. J. Elliott, "Impact of Image Quality on Performance: Comparison of Young and Elderly Fingerprints," presented at 6th International Conference on Recent Advances in Soft Computing (RASC), Canterbury, UK, 2006.

[6] E. Tabassi and C. L. Wilson, "A novel approach to fingerprint image quality," presented at International Conference on Image Processing, Genoa, Italy, 2005.

[7] M. Yao, S. Pankanti, and N. Haas, "Fingerprint Quality Assessment," in Automatic Fingerprint Recognition Systems, N. Ratha and R. Bolle, Eds. New York: Springer, 2004, pp. 55-66.



[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.


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