• Like
(2011) The Impact of Force on Fingerprint Image Quality, Minutiae Count and Performance
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.

(2011) The Impact of Force on Fingerprint Image Quality, Minutiae Count and Performance


The impact of force on acquiring a high quality …

The impact of force on acquiring a high quality
fingerprint image has been systematically studied by researchers
using single print optical scanners. A previous study examined
force levels that ranged from 3N to 21N using a single print
optical sensor. A second experiment used force levels ranging
from 3N to 11N using a capacitive and optical single print sensor.
Additional work has been conducted that looked at smaller
increments of force also using an optical sensor. This paper
contributes to the body of knowledge by using an alternative
fingerprint sensor, an alternative force level and compares it to
the auto-capture method.

Published in Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads


Total Views
On SlideShare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 1. The Impact of Force on Fingerprint Image Quality, Minutiae Count and Performance Michael Petrelli, Stephen Elliott, Carl Dunkelberger Department of Technology, Leadership and Innovation Purdue University West Lafayette, IN 47907 Abstract— The impact of force on acquiring a high quality fingerprint image has been systematically studied by researchers using single print optical scanners. A previous study examined force levels that ranged from 3N to 21N using a single print optical sensor. A second experiment used force levels ranging from 3N to 11N using a capacitive and optical single print sensor. Additional work has been conducted that looked at smaller increments of force also using an optical sensor. This paper contributes to the body of knowledge by using an alternative fingerprint sensor, an alternative force level and compares it to the auto-capture method. Keywords-fingerprint, image quality I. INTRODUCTION In fingerprint recognition systems, the quality of an acquired fingerprint has a significant impact on overall system performance. There are a number of factors that contribute to the performance of a biometric system. These factors can come from a number of different sources, whether that be from the subject, the test administrator, or the system [1]. One of the greatest limitations in acquiring quality images from fingerprint devices involves both inconsistent surface contact and non- uniform contact, between the finger and device platen [2]. Placement variables such as horizontal and vertical translation, rotation around the scanner, roll and orthogonal placement, torque and applied force can be manipulated at the moment of interaction [3-6]. The manipulation of the amount of force the subject places on the platen should reduce these distortions and therefore provide more consistent images from the subject. There has been some research on this topic, for example [7] conducted two experiments; the first using four force levels (3N, 9N, 15N, and 21N) and the second using five force levels (3N, 5N, 7N, 9N, and 11N). In that research, the recommended force levels that an individual should apply to a single print optical sensor would be approximately 5N – 7N. Another study [8] used a different optical sensor and chose force levels ranging from 1.5N to 7.5N in increments of ± 0.5N. In that study 5.5N was identified as an optimal force level for the performance of that particular sensor. Both of these studies used the index finger. The motivation behind this paper is to examine the performance of a different optical fingerprint sensor, as well as to see how force impacts image quality and minutiae distribution across different fingers. II. METHODOLOGY A commercially available 10-print fingerprint scanner was placed with its base 39 inches off of the ground, which was selected because that was the most common height of the table where this type of scanner is used. The recommendations of [9] were also considered in the design of the study. Subjects were required to stand during the fingerprint capture process. Once the subjects had completed the necessary consent procedures, they completed the various demographic questions (see Table 1), followed by a demonstration of the process by the test administrator. The subject was allowed to practice with the sensor. A number of variables were of interest, including the image quality score, minutiae count, time to complete the process, False Reject Rate at 0.1%, 0.01%, and 0.001% FAR levels, and failures from either an acquisition or extraction of the image. The experiment consisted of two fingerprint capture methods referred to as auto-capture and force-capture. Three images were collected from the right and left index, middle, and thumbs for both the auto-capture and force method. The first method utilized the CrossMatch Guardian L Scan 500’s proprietary auto-capture system to collect images. This method is referred to as auto-capture. In this method, individuals were not allowed to see their image or given feedback on how hard they were pressing. The second method captured images based on how hard the individual was pressing on the platen. The capture event occurred only after a fingerprint was detected on the platen within the force range of between 5N – 7N. This method is referred to as force-capture. Due to the nature of the setup, all subjects completed the auto-capture data collection before moving onto the force level study. The subject was allowed to see how hard they were pressing and the feedback was provided on a large textbook indicating applied force in Newton’s. As in the auto-capture method, the subject was not able to see their image, or given feedback apart from the force display. A timeout occurred if the subject took more than 30 seconds. The subjects had to disengage from the sensor after every interaction. After the subject had completed the auto- capture data collection, they were asked to complete a survey that asked them about the comfort level of the device. The subject then returned to the data collection process and completed the force component. 978-1-4577-0490-1/11/$26.00 ©2011 IEEE
  • 2. The data collection system was composed of a platform, force sensors, USB Analog and Digital Input/Output (I/O) module, fingerprinting scanner, personal computer and user interface. The platform consisted of a 6.5”x6.5”x0.25” piece of compressed wood to provide an area to place the fingerprint scanner and force sensors. The force sensors consisted of Honeywell FSS1500 High Precisions Force Sensors located at each of the four corners of the platform. These force sensors were both powered and measured by Measurement Computing’s USB-based Analog and Digital I/O Module USB-1408FS. The fingerprint scanner was a Guardian LScan 500 by CrossMatch Technologies. The I/O module and fingerprint scanner both connected to a single core 2GHz personal computer with 2Gb of random access memory. III. RESULTS AND ANALYSIS Data was collected from 70 individuals; the breakdown of the demographic information is shown in Table 1. The majority of subjects (87%) were college aged, with 91% self-selecting office worker as their occupation category. Seventy-four percent of the subjects had previous experience with fingerprint sensors in general. This was expected, as there were a number of studies being conducted on fingerprint sensors during that time. Furthermore, there are several businesses aimed at the college population that have optical fingerprint sensors in the local community (e.g. a tanning salon has a fingerprint sensor installed for identification). Seventy seven percent of the subjects had not had experience with this specific optical sensor. Table 1: Demographic Information Category Responses Number Percentage Age 18-29 61 87% 30-39 5 7% 40-49 1 1% 50+ 3 4% Ethnicity African- American 1 1% Asian 25 36% Hispanic 1 1% Latino 1 1% Native American 1 1% Caucasian 41 59% Gender Male 23 33% Female 47 67% Labor Distribution Manual 6 9% Office 64 91% Dominant Hand Left 5 7% Right 65 93% Missing Finger or Musculoskeletal condition? No 69 1% Yes 1 99% Prior No 18 26% Experience with Single Print Scanner Yes 52 74% Prior Experience with Ten Print Scanner No 54 77% Yes 16 23% 1260 images were collected from the right and left index fingers, middle fingers, and thumbs, and were compared to 1258 images captured using the force-capture method. The breakdown of these images can be found in Table 2. TABLE 2: FREQUENCY OF FINGERS ACROSS AUTOCAPTURE (AC) AND FORCE CAPTURE (FC) RI RM RT LI LM LT AC 210 210 210 210 210 210 FC 210 209 210 210 210 209 A. Image quality analysis The first stage of the analysis was to examine image quality. As the data was non-normal, a Kruskal-Wallis was used to determine if there were any significant differences among the population medians. Whilst the test statistic is a general hypothesis to examine whether all the population medians are equal, an effective way of doing pairwise simultaneous inferences can be completed by using a Dunn’s Test. Figure 1 shows the box plots of the finger image quality fingers (LI, LM, LT, RI, RM, RT)on the left side of the image. To the right are the pairwise comparisons, which show the magnitude of the group differences and their direction. Table 3 illustrates the groups that showed significant differences (adjusted for ties). Figure 1a: Dotplots with Sign Confidence Interval
  • 3. Figure 1b: Pairwise Comparisons TABLE 3: KRUSKAL-WALLIS CONCLUSIONS Groups Z vs. Critical Value P-value LI vs. LR 4.97192 >= 2.475 0.0000 LR vs. RM 4.41433 >= 2.475 0.0000 LM vs. LR 4.26114 >= 2.475 0.0000 LR vs. RI 4.03135 >= 2.475 0.0001 LI vs. RT 2.97527 >= 2.475 0.0029 The next stage of the analysis was to examine whether there is any significant difference between auto-capture and force levels. Again, the data was non-normal, so a Kruskal-Wallis test was conducted. The sample medians for the two treatments were calculated as 86 and 87. The test statistic (H) had a p-value of 0.005, both unadjusted and adjusted for ties, indicating that the null hypothesis can be rejected at α=0.05. The higher median score was observed by the force-capture algorithm. Table 4 shows the Mann-Whitney test results. TABLE 4: IMAGE QUALITY Finger (force vs. autocapture) p-value Left Index 0.0065 Left Middle 0.0084 Left Thumb 0.0858 Right Index 0.0173 Right Middle 0.0041 Right Thumb 0.0695 The results in Table 4 show that there are significant differences in the quality of the image for force and auto- capture (at α=0.05), except for thumbs. The data from the right and left thumb do not support the hypothesis that there is a difference between the population medians. B. Minutiae analysis The numbers of minutiae were detected using commercially available image quality software package. 1260 images from the auto-capture method were compared to the 1258 images from the force-capture method. Due to the lack of normality, a non-parametric test was conducted. Table 5 shows the results of differences of minutiae across the two force levels. TABLE 5: MINUTIAE ANALYSIS Finger (force vs. autocapture) p-value Left Index 0.8269 Left Middle 0.2347 Left Thumb 0.0003 Right Index 0.0075 Right Middle 0.0606 Right Thumb 0.0206 The results here are mixed. The minutiae counts of the left index, left middle, and right middle do not support the hypothesis that there is a difference across the medians. Left thumb, right thumb and right index do support the hypothesis that there are differences across the medians. Further research identifying the differences in the minutiae counts is recommended. C. Failure to Acquire and Failure to Extract The next test was to determine whether or not the proportions of failures to acquire were different between capture methods. A failure to acquire was defined as “the expected proportion of transactions for which the system is unable to capture or locate an image or signal of sufficient quality” [10]. A failure to acquire occurred when the capture process took longer than 30 seconds. Failure to locate an image of sufficient quality is referred to as failures to extract. The frequencies of these occurrences are shown in Table 6. TABLE 6: FREQUENCY OF FAILURE OCCURRENCE Auto- Capture % Force- Capture % Failures to capture within 30 seconds 0 0% 1 0.08% Failures to extract 34 2.70% 48 3.81% Total 34 2.70% 49 3.89% The force-capture method observed 15 more failures to acquire than the auto-capture method with 2.7% failures to acquire as compared to the 3.89%. This may be because of the higher tolerance of acceptable images associated with that algorithm. Because failures to acquire deals with proportions, a two sample proportion test was completed to compare the
  • 4. success rates between the two capture methods. The results indicate no statistically significant difference between capture methods for proportion of failures to enroll with a p-value of 0.092. D. Process Time Timer values were logged after each successful capture event. The start time for the capture process began at the command to place the finger on the platen and was stopped when the finger image was captured. Thirty successful capture log entries were removed from the data set as these subjects did not follow the instructions on finger placement. Several of the subject interactions caused long process times. One large spike of six large process times came from one subject. The results show that there was a statistically significant difference in the time required to process an image with a p-value of 0.000. E. Habituation Habituation may have affected how quickly an image was captured between the first, second, and third interactions, as well as across capture methods. As a subject uses a device, they may become more familiar with the device as the data collection procedure progressed. As the subjects processed through autocapture and then the force process, the issue of acclimation to the device may compromise the results. Although the results above showed that there was a significant difference in the process times across force and autocapture, there was no difference in process time between fingers. If habituation was present, then we would expect to see the process time decrease across each interaction. The process time between fingers in the autocapture method was p=0.323 and the force method were 0.091. Furthermore, if habituation was a factor, it might be expected that the number of failure to acquire would be greater for force as opposed to autocapture. F. Matching Performance Matching performance was compared by generating a Detection Error Tradeoff (DET) curve and testing false accept rates at 0.1%, 0.01% and 0.001% FAR. The results are shown in Figure 2, with autocapture denoted by the blue line, and force capture denoted by the red line. Figure 2: Detection Error Tradeoff Curves The FRR comparison at 0.1% FAR is shown below in TABLE 7: FRR COMPARISON AT 0.1% FAR Auto-capture FRR Force-capture FRR FAR 0.1% 0.41% 0.49% FAR 0.01% 0.41% 0.50% FAR 0.001% 0.41% 31.6% The results show that force-capture method has a slightly worse matching performance compared to the auto-capture method at the 0.1% and 0.01% FAR. However, at the 0.001% FAR, the force-capture at 5-7N was worse by a significant margin. IV. CONCLUSIONS AND RECOMMENDATIONS This paper provided results of a commercially available optical sensor for a single-print. Adding to the body of knowledge, this particular optical sensor did not provide better performance at the 5-7N force level, although these differences at FAR 0.1% and 0.01% were not significant. Furthermore, additional insight was provided by examining the individual finger and the impact of force on image quality and minutiae count. Further research should investigate whether there is any significant impact in the performance of individual fingers across force levels in general. An interesting conclusion of this paper shows that in the case of optical fingerprint sensors, there are disparities in the optimal level of force; a further study should examine interoperability of fingerprints across different optical sensors at different force levels. REFERENCES [1] A. Hicklin and R. Khanna, “The role of data quality in biometric systems,” White Paper. Mitretek Systems (February 2006), no. February, 2006. [2] E. Tabassi and C. L. Wilson, “A novel approach to fingerprint image quality,” in IEEE International Conference on Image Processing, 2005. ICIP 2005, 2005, vol. 2, pp. 37-40. [3] Atos Origin, UK Passport Service Biometrics Enrolment Trial. 2005, p. 299. [4] E. Kukula, S. Elliott, H. Kim, and C. San Martin, “The impact of fingerprint force on image quality and the detection of minutiae,” in Electro/Information Technology, 2007 IEEE International Conference on, 2007, pp. 432–437. [5] A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics, vol. 14, no. 1, pp. 4-20, Jan. 2004. [6] S. K. Modi, S. J. Elliott, J. Whetsone, and H. Kim, “Impact of Age Groups on Fingerprint Recognition Performance,” in 2007 IEEE Workshop on Automatic Identification Advanced Technologies, 2007, pp. 19-23. [7] B. Senjaya, S. J. Elliott, S. K. Modi, and T. B. Lee, “Examination of Fingerprint Image Quality and Performance on Force Acquisition vis-a-vis Auto Capture,” in 44th Annual IEEE International Carnahan Conference on Security Technology, 2010, pp. 237-242.
  • 5. [8] E. Kukula, S. Elliott, and C. San Martin, “The impact of fingerprint force on image quality and the detection of minutiae,” in 2007 IEEE International Conference on Electro/Information Technology, 2007, pp. 432-437. [9] M. Theofanos, S. Orandi, R. Micheals, B. Stanton, and N. Zhang, “Effects of Scanner Height on Fingerprint Capture.” National Institute of Standards and Technology, p. 58, 2006. [10] A. J. Mansfield and J. L. Wayman, Best Practices in Testing and Reporting Performance of Biometric Devices ver 2.01. Teddington: , 2002, pp. 1-36.