Fingerprint Recognition Performance Evaluation for Mobile 10 Applications

Shimon Modi, Ph.D. BSPA Lab Purdue University 401 N. Grant Street West Lafayette, IN, 47907 USA

Ashwin Mohan BSPA Lab Purdue University 401 N. Grant Street West Lafayette, IN, 47907 USA

Abstract - According to a report by Frost and Sullivan in 2007, revenues for non-AFIS fingerprint devices in notebook PC's and wireless devices is anticipated to grow from $148.5 million to $1588.0 million by 2014, a compound annual growth rate of 40.3% [1]. The AFIS market has a compound annual growth rate of 15.2% with revenues of $445.0 million in 2007. With the development of mobile applications in a number of different market segments, such as healthcare, retail, and law enforcement, this paper analyzed the performance of fingerprints of different sizes, from different sensors (commercialy available optical and capacitance) which were generated in accordance with NIST Mobile 10 best practices document using an automated image cropping process. The minutiae count, image quality scores, false non match rates (FNMR) and false match rates (FMR) were evaluated for images of seven different image sizes, ranging from 0.098"XO.126" to 0.578"XO.618". The results provide insight into constraints of fingerprint image sizes, interoperability and fingerprint matching of different sizes in a mobile 10 environment.

Index Terms - mobile 10 biometrics, fingerprint recognition performance, minutiae count.

I. INTRODUCTION

The need for stronger authentication and identity management has lead to an increased use of biometric technologies. Traditional authentication technologies like passwords and tokens are based on something that a person knows or possesses, respectively. Biometric technologies use behavioral and biological characteristics of humans for authentication [2]. Biometric technologies have been used in law enforcement applications ever since Henry formulated his system of fingerprint recognition in the 1890's [3]. Traditionally law enforcement agencies have captured fingerprints when an individual is brought to the booking station which allows for greater control over the fingerprint acquisition process. Today, increased connectivity outside of the traditional police booking area allows law enforcement agents to use mobile devices for a diverse range of activities in the field. Various law enforcement agencies have started exploring the use of mobile devices to capture fingerprints, face and iris images from individuals at remote locations and sending the information to a central server for further processing. Andhra Pradesh, a state in India, has deployed a fingerprint analysis and criminal tracing system based on a centralized storage with decentralized acquisition architecture, with the specific

978-1-4244-7402-81101$26.00 ©2010 IEEE

Benny Senjaya BSPA Lab Purdue University 401 N. Grant Street West Lafayette, IN, 47907 USA

Stephen Elliott BSPA Lab Purdue University 401 N. Grant Street West Lafayette, IN, 47907 USA

intent of capturing information from the field and checking it against a central repository [4]. Mobile devices give law enforcement agencies the advantage of capturing biometric information in various locations without the inconvenience of bringing the subject back to a central processing location [5]. Using mobile devices to capture biometric data can improve the effectiveness of agents by providing the capability of identifying people of interest in the field and generally improving workflow. But there are several technical and operational challenges to implementing a mobile device biometric system. Mobile devices embedded with biometric technologies currently do not have a standardized sensor capture area thereby producing different sized images. Knowledge of the optimal size of the fingerprint image and its impact on quality and performance of the overall system can make the system more efficient. The research presented in this paper evaluated the impact of fingerprint size and fingerprint sensor interoperability on recognition performance. The purpose of this research was to examine image quality, minutiae count and performance in terms of matching error rates of different sized fingerprint images collected from an optical and capacitive fingerprint sensor. The objective of this assessment was to provide performance related information on feasibility of using mobile fingerprint devices in the field and thereby improving best practices for such devices.

II. LITERATURE REVIEW

One of the earliest works in evaluating impact of image size on fingerprint recognition performance was performed by [6]. Watson and Wilson examined the effect of cropping fingerprint images on the ability to match the fingerprints. The conclusion of their evaluation was to not use fingerprint images of less than 320X320 pixels for matching. They also concluded that compression degrades performance at a slower rate than the size of the fingerprint image.

In [7], Schneider et aI., described results of a study that analyzed correlation of fingerprint image size on matching performance. They used a dataset of images collected from 259 people with an ultrasonic fingerprint reader. They cropped their fingerprint images using a center point calculated by two different methods. The first method used a fixed pOint on the platen of the sensor as the cropping center point. The second method calculated the center of mass using centroiding techniques. Their results showed a decreased performance with a decrease in image size. This study demonstrated correlation between image size and performance, but did not attempt to analyze impact of quality

on performance of images of different sizes. Ortega-Garcia et.al in [8] described a speech and

fingerprint multimodal matching system where a mobile device is used to collect the biometric data. The data was evaluated using three different multimodal fusion techniques and an adaptive fusion strategy, and the results showed an improvement in all strategies thus demonstrating the feasibility of using biometric data collected from mobile devices.

Interoperability of fingerprint sensors is an issue that has an impact on performance of fingerprint recognition [9]. The variations and distortions introduced by different acquisition technologies are not consistent which generates matching errors. Most commercially available optical sensors are based on the concept of Frustrated Total Internal Refraction (FTIR) which is affected by skin distortion, outdoor lighting and residue on the sensor platen [10]. Most capacitive sensors measure the difference in capacitance on a charged surface when a finger comes in contact with it. The difference in capacitance corresponds to the ridges and valleys of the fingerprint [11]. The impact of sensor interoperability can be reduced by improving the quality of fingerprint images and fusing features of fingerprints collected from different sensors [12]. This study expanded on the work performed by [6] and [7] with respect to image sizes by analyzing images collected from different acquisition technologies and including minutiae count and image quality information in the analysis framework.

III. DATA SET & METHODOLOGY

Fingerprints from 190 subjects were used for analysis in this study. Six fingerprint images were collected from the index finger of the subject's dominant hand using two different fingerprint sensors. The fingerprints were collected in a single session and the subjects were seated during the fingerprint acquisition session. A large area capacitive touch sensor and optical sensor were used in this study. Both sensors were FIPS 201 certified for single capture devices. Table 1 gives a summary of the participant demographics.

Total Participants Gender

Occupation

Number of samples

TABLE I DATASET SUMMARY

190 Male 131

Manual Laborer 17

Female

59

Office Worker

173 190'6 - 1140

In order to test the effect of image size on image quality and performance, seven different image sizes were selected. The original image collected during the data collection session was cropped to form the images for these seven different sizes. The image sizes were chosen from page 17 of the Mobile ID Device Best Practice Recommendation [3]. A Matlab ™ application developed in-house was used to crop the images using the core values as the center of the cropping region. If a fingerprint image had two cores then the core with the higher y-axis coordinate was chosen. Table 2 lists the image sizes and image examples from the 2 sensors. Please note the

samples images shown here are not the original size images. These samples are an illustration of the amount of detail in each image. It was observed that images at level 7 were large enough to capture the entire fingerprint image.

TABLE II SAMPLE IMAGES

Image Size Level

0.098" X 0.126"

0.118" X 0.154" 2

0.154" X 0.194" 3

0.31 4

5

7

The purpose of this research was to examine image quality, minutiae count and performance in terms of matching error rates of different sized fingerprint images collected from an optical and capacitive fingerprint sensor. One of the objectives of this study was to simulate matching of fingerprints collected by different sensors and of different sizes, the fingerprint were cropped to simulate the size of images that would be collected from a mobile device.

IV. DATA SET & METHODOLOGY

A. Minutiae Count

This step of the analysis was to examine the average minutiae count for each of the seven image sizes noted above in Table 2. Minutiae counts were generated using Neurotechnology's VeriFinger 6.0 extractor. The results indicated that fingerprint images from image size level 4 (O.31"X 0.29") and above had an average minutiae count higher than 10.

Level

1

2

3

4

5

6 7

TABLE III AVERAGE MINUTIAE COUNT

Optical Capacitive Dataset Dataset

1.98 2.04

2.91 2.92

4.28 4.32

11.04 10.83

15.11 15.06

16.54 16.37

32.31 32.12

The number of minutiae is fundamental to the process of matching fingerprints. The 12-point guideline used in forensic science states that assuming an expert can correctly extract all minutiae points from a latent fingerprint, a 12-point match with a full fingerprint can be considered as a sufficient evidence of fingerprint matching [13]. Figures 1 and 2 show the minutiae histograms across the seven different image sizes.

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B. NFIQ Image Quality

Image quality scores using the NFIQ image quality algorithm were calculated [14]. NFIQ generates quality scores in the range of 1-5 where 1 indicates best possible score and 5 indicates the worse possible score. NFIQ scores are predictive of performance that should be expected for the fingerprint image. Due to the extremely low minutiae count for levels 1, 2 and 3 (Table 3) image quality scores were generated only for fingerprints at levels 4, 5, 6, and 7. The raw images were converted to WSQ format at 15:1 ratio using a certified package [15]. Table 4 gives a summary of descriptive statistics at each level.

TABLE IV NFIQ SCORE DESCRIPTIVE STATISTICS

Level Optical Dataset Capacitive Dataset

4 Median : 3.0 Median : 3.0

Mean: 3.0 Mean : 3.0

5 Median :3.0 Median :3.0

Mean:2.8 Mean:2.7

6 Median: 3.0 Median: 3.0

Mean 2.8 Mean: 2.7

7 Median : 2.0 Median : 2.0

Mean:1.9 Mean:1.7

Quality assessment is important to ensure high quality fingerprint images are stored for matching purposes. Previous research has shown that the effect of low quality images is predictable, but the impact of high quality images is harder to determine. Images at levels 4, 5, and 6 had the same median score and images at level 7 had the best NFIQ median score. Both the optical and capacitive data sets exhibited similar behavior for image quality scores and average minutiae count.

C. Performance of Native Datasets

To compare performance of biometric systems a modified Receiver Operating Characteristic (ROC) curve called Detection Error Tradeoff (DET) curve can be used [16]. A DET curve plots the false match rate (FMR) on the x-axis and false non match rate (FNMR) on the y-axis as function of decision threshold.

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Figure 3 and 4 show superimposed DET curves for the image size levels 1 to 7 for the optical and capacitive data sets respectively. Tables 5 and 6 list the FNMR and FMR respectively at an operational threshold of 0.1 % FMR using the VeriFinger matcher.

TABLE V VERI FINGER FNMR (IN %)

ImaQe Size Level Optical Dataset Capacitive Dataset

1 98.52 98.37

2 81.76 81.00

3 46.24 41.25 4 1.27 1.70

5 0.00 0.58

6 0.12 0.29

7 0.00 0.00

TABLE VII VERI FINGER FMR (IN %)

ImaQe Size Level Optical Dataset Capacitive Dataset

4 0.28 0.26 5 0.30 0.23

6 0.16 0.14 7 0.00 0.01

Both DET curves showed an improvement in performance as the size of the images was increased. The best performance for the fingerprint images was found to be at level 7 for both the optical and capacitive datasets. The results from Tables 3 and 4 showed that images at level 7 had the best quality and the highest average of minutiae count which resulted in lower number of false non match and false match errors. This was expected since level 7 covered the entire fingerprint region. The median NFIQ scores for images at levels 4, 5 and 6 (Table 4) showed a similar median score but the DET curves showed a marked improvement in performance for both the optical and capacitive data sets. Although the median image quality score at level 4 and 5 were the same, the average number of minutiae count increased which lead to a decrease in the FNMR.

D. Zoo Plot Analysis

The relation between genuine score distribution and imposter score distribution can be analyzed using the biometric zoo plot as described by Dunstone and Yeager [17). They described four different categories based on the relationship between genuine and imposter scores:

Chameleons, Phantoms, Doves and Worms. Chameleons generate high genuine and imposter scores. Phantoms generate low genuine and imposter scores. Doves generate high genuine scores and low imposter scores. Worms generate 1?"¥J!enuine score� and high imposter scores.

Performlx , a commercially available software package provides the ability to visually analyze large biometric datasets, was used to generate so called "zoo plots". Fig. 5 and 6 are the zoo plots for level 7 datasets for optical and capacitive sensors.

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Both figures 5 and 6 show a weak positive relationship between average genuine match score and average imposter score for the datasets. The graphs show a higher probability of finding worms (the bottom left quadrant) indicating that subjects were generating low genuine and imposter scores. Low genuine scores will result in a lower confidence in the matching operation, as well as a higher probability of false non matches, which in a law enforcement application is not desirable. Analysis of the zoo plots did not reveal any specific trends for any of the zoo categories. Similar zoo plots were generated for level 5 and level 6 for optical and capacitive data sets and while the overall relationship remained stable, the individual data points were more widely dispersed. Those subjects that generated the highest number of false matches were identified from the zoo plot. For optical dataset and capacitive dataset level 7, two subjects were found to have

produced 36 false matches but they were not generated against the same subjects. The same analysis was performed on level 5 and 6 for both datasets to examine trends in subjects generating false matches and false non-matches. The results are given in Table 7.

Table 1. Veri Finger FMR (in %)

Level Subject Number of ID(s) False Matches

Optical L7 56,70 36

Capacitive L7 58,178 36

Optical L6 79,178 160

Capacitive L6 58,1 186

Optical L5 4,58 252

Capacitive L5 36,87 192

For images at level 7, 6 and 5 there were no common subjects between optical and capacitive datasets. Identifying subjects that generate errors in the optical dataset would not be useful in identifying subjects that generate errors in the capacitive dataset and vice-versa. It was also observed that there were no common subjects for the optical dataset across different levels. The same observation holds true for the capacitive dataset across different levels.

E. Performance of Interoperable Datasets

In a mobile 10 infrastructure, the ability to match fingerprints from different sensors is important. Previous research has shown that matching fingerprints from different sensors has an effect on error rates [18]. Interoperability analysis was performed to evaluate the FNMR of matching fingerprints from the optical and capacitive datasets. The analysis concentrated on FNMR as a first step towards a comprehensive analysis of performance analysis. VeriFinger 6.0 was used to generate FNMR. Every subject provided 6 images on each sensor. The interoperable dataset was generated by combining 3 images from the optical dataset with 3 images from the capacitive dataset. FNMR was calculated for the interoperable datasets at every image size level by comparing the optical fingerprint images to capacitive fingerprint images. Table 8 shows the results of the interoperable FNMR at fixed 0.1 % FMR.

Table 2. FNMR (in %) at FMR of 0.1%

Image Size Level Interoperable Dataset

1 99.79 2 91.42 3 61.34 4 4.56 5 1.03 6 0.27 7 0.27

Interoperability FNMR reduced significantly between level 3 and level 4, which can be attributed to higher number of minutiae points for level 4 images. This improvement indicates the need for larger size images when comparing images from

different sensors. The interoperability FNMR for images for level 5 was different from level 6 and this seems to have been caused in part by the higher number of minutiae points and not due to image quality scores. This indicates a need for a an assessment framework of input fingerprint images which takes into account factors other than just image quality scores.

F. Matching Performance for Different Sized Images

The size of fingerprint images collected from the field will not remain constant. The size of the fingerprint image is a function of the type of mobile device used for capturing the fingerprint images. The analysis in this section focused on understanding the impact of comparing fingerprint images of different sizes. Images at level 7, (0.578"XO.618") - the largest size were considered to be the enrollment or reference template. Fingerprints from levels 3, 4, 5 and 6 were compared to the reference template and the FNMR was calculated, which are shown in Table 9.

Table 3. FNMR (in %) at FMR of 0.1 %

Enrollment Level Comparison Optical Capacitiv Level Dataset e Dataset

FNMR% FNMR%

3 70.28 68.53 7 4 2.09 2.16

5 0.12 0.26

6 0.24 0.26

The lowest FNMR was generated when images from level 5 and 6 were compared to the enrollment images. Both optical and capacitive fingerprint images showed a similar trend in decrease of FNMR among the different levels of images. The ability to match fingerprints of different sizes is crucial, and even more so in a mobile device environment. When results from Table 5 are compared with results from Table 9 it can be seen that matching two fingerprints from image size level 4 yields better results than matching fingerprints of level 7 to level 4. It was also observed that matching fingerprints from different image size levels had better results than matching fingerprints from different sensors. Operational decisions about mobile fingerprint devices will depend on the performance of fingerprints captured, and these results will aid in forming policies for comparing fingerprints of different sizes and fingerprints collected from different sensors. For example, comparison of fingerprints from level 7 and 3 should not use a single fingerprint to make a decision, whereas confidence in comparison of fingerprints from level 7 and 4 would be increased by using 2 or more fingers.

V. CONCLUSIONS

The results from this research show that single fingerprint images of sizes at or below level 3 are unsuitable for matching purposes. For mobile fingerprint devices the number of minutiae extracted from the image is as crucial as capturing high quality fingerprint images. Although fingerprints at level 7 showed the best results, fingerprint images at levels 5 and 6 also showed performance that would be acceptable in law enforcement applications. Interoperability FNMR reduced as the size of the fingerprint image was increased, which indicates that the number of minutiae was important to

reducing FNMR. An interesting result was the similarity of the interoperability FNMR at level 6 and level 7, even though the number of minutiae count and NFIQ score was significantly different at the two levels. The comparison of fingerprint images across different levels also showed acceptable FNMR at levels 5 and 6.

The results from this research have shown the different operational issues that could arise due to use of fingerprint recognition in a mobile 10 environment. There are several other avenues of research which require attention to get a better understanding of mobile fingerprint recognition. This study used images collected using peripheral fingerprint scanners. Collecting fingerprint images using mobile devices would also highlight usability issues, data collection errors and differences between providing fingerprint sequentially or simultaneously to the fingerprint devices. This study used single fingerprint comparisons for calculating FNMR. Future studies should examine the improvement in performance by using 2, 3, and 4 finger matching. Another important aspect in mobile 10 is the data transfer rates between the mobile device and the central processing station. The size of the data packets would determine the processing time, and the impact of different WSQ compression levels at each size level also needs to be analyzed. The INC ITS 378 template standard was not used to store the fingerprint details in this study. The impact of standardized templates also requires evaluation in order to ensure interoperability between different systems. As the deployments of mobile fingerprint devices increases further research into these operational issues will become crucial to its success.

[1]

VI. REFERENCES

"Non-AFIS Fingerprint in Notebook PC's and Wireless Market Devices," 2008.

[2] "ISO/IEC JTC1 SC37 SD2 - Harmonized Biometric Vocabulary," 2006.

[3]

[4]

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[7]

[8]

R. Bolle, S. Cole, and N. Ratha, "History of Fingerprint Pattern Recognition," New York: Springer, 2004, pp. 1-25.

CMC, "Fingerprint Analysis and Criminal Tracing System."

Mobile ID Device Best Practice Recommendation, NIST, 2008.

C. Watson and C. Wilson, "Effect of Image Size and Compression on One-to-One Fingerprint Matching," 2005.

J. Schneider, C.E. Richardson, F.W. Kiefer, and V. Govindaraju, "On the Correlation of Image Size to System Accuracy in Automatic Fingerprint Identification Systems. in Audio-and Video-Based Biometrie Person Authentication," Audio-and Video­Based Biometrie Person Authentication, Guildford, U.K.: LNCS, 2003.

J. Ortega-Garcia, J. Fierrez-Aguilar, J. Bigun, and J. Gonzalez-Rodriguez, "Multi modal Biometric Authentication using Quality Signals in Mobile Communications," Mantova, Italy: 2003.

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VIII. VITA

Shimon Modi graduated from Purdue University in 2008 with Ph. D. in Technology focusing on evaluation of biometric system interoperability. He has been the Director of Research of BSPA Lab, Purdue University and he is currently a visiting scientist with Center for Development of Advanced Computing (C-DAC) Mumbai, India. He has written several papers on statistical evaluation of biometric systems with respect to interoperability, sample quality and demographics.

Ashwin Mohan is a graduate student currently pursuing the M.S. degree in Information Security from the Centre for Education and Research in Information Assurance (CERIAS) at Purdue University. He received his Bachelors degree from the Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), India. His research interests include fingerprint recognition, development of biometric standards and evaluation of small scale device forensics.

Benny Senjaya is a graduate student currently pursuing the M.S. degree in Technology focusing on human interaction within biometric technology at Purdue University. He received

his Bachelors degree from Purdue University on Computer and Information Technology specializing in Network Engineering Technology. His research interests include human interaction, iris recognition, and fingerprint recognition technology.

Stephen J. Elliott is currently an Associate Professor with the Department of Industrial Technology, Purdue University, where he is also a University Faculty Scholar and the Director of the Biometric Standards, Performance, and Assurance Laboratory. He received his Ph. D. from Purdue University in 2001. He has spoken at several conferences and is active in biometric standard initiatives. He is the Editor of ANSI/INCITS Information Technology-Biometric Performance Testing and Reporting-Part 5: Framework for Testing and Evaluation of Biometric System(s) for Access Control. He has written numerous articles on biometrics. His research interests include the testing and evaluation of biometric systems.