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(2009) Comparison Of Fingerprint Image Quality And Matching
 

(2009) Comparison Of Fingerprint Image Quality And Matching

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Research has shown for some age groups, quality of fingerprints can impact the performance of biometric systems. A ...

Research has shown for some age groups, quality of fingerprints can impact the performance of biometric systems. A
desirable feature of biometrics is that they are suitable for use across the population. This applied study examines the performance of a fingerprint recognition system in a healthcare environment. Anecdotal evidence suggested front line healthcare workers may have lower image quality due to continued hand washing which may remove oils from their skin. During training, individuals are told to add oil to their fingers by wiping oil from their foreheads to improve the resulting quality of the
fingerprints. In the healthcare population the authors tested, compared to two general populations (collected on optical and
capacitance sensors) there was a significant difference in skin oiliness, but not in image quality. There was a difference across
healthcare and non-healthcare groups in the performance of the fingerprint algorithm when compared against the capacitance dataset.

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    (2009) Comparison Of Fingerprint Image Quality And Matching (2009) Comparison Of Fingerprint Image Quality And Matching Document Transcript

    • A Comparison of Fingerprint Image Quality and Matching Performance between Healthcare and General Populations Christine R. Blomeke, Stephen J. Elliott, Ph.D., Benny Senjaya, and Gregory T. Hales Abstract— Research has shown for some age groups, quality of in the deployment of biometric systems in many different fingerprints can impact the performance of biometric systems. A environments and applications, and one area that has gained desirable feature of biometrics is that they are suitable for use attention in the United States is the healthcare environment, across the population. This applied study examines the especially post-HIPAA – the Health Insurance Portability and performance of a fingerprint recognition system in a healthcare environment. Anecdotal evidence suggested front line healthcare Accounting Act. In many medical practices and hospitals, the workers may have lower image quality due to continued hand increased use of tablet PCs by physicians to access electronic washing which may remove oils from their skin. During training, medical records lends itself to being secured through the use individuals are told to add oil to their fingers by wiping oil from of biometrics. This would help to restrict access to electronic their foreheads to improve the resulting quality of the medical records to those with the proper credentials. fingerprints. In the healthcare population the authors tested, Likewise, the availability of tablet PCs with biometric sensors compared to two general populations (collected on optical and capacitance sensors) there was a significant difference in skin built into them provides a solution that would not require oiliness, but not in image quality. There was a difference across additional hardware. One of the motivators to this short healthcare and non-healthcare groups in the performance of the research project was to investigate the claims of those that fingerprint algorithm when compared against the capacitance have worked in the healthcare community near West dataset. Lafayette, IN., that some deployed or trialed fingerprint biometrics seem to have some issues with users, specifically Index Terms—fingerprint recognition, image quality, front line nursing staff within that environment. The performance, healthcare. motivational question to ask is whether the biometric modality is not suitable to the healthcare environment, or whether the I. INTRODUCTION proposed deployment was deficient in other areas. The Biometrics, the automatic identification of individuals based authors initially thought that due to the nature of the clinical on their physical or behavioral characteristics have been environment, especially glove wearing, and the need to proposed and implemented for a number of different continually wash hands might lead to dryer skin which in turn applications. These include single-sign on applications in might have an effect on the performance of the fingerprint education, financial, and industrial applications. There are biometric system. Combined with any human-biometric several desirable biometric attributes, both from a user, sensor interaction difficulties or lack of habituation from the system, and biometric modality perspective. One such users, this could lead to a domino effect. This study examined attribute is that the biometric system works with the proposed 30 individuals from a healthcare environment and compared population. Over the past eight years there has been a growth them two datasets of 30 individuals, one dataset collected on optical sensor, the other on a capacitance sensor. Manuscript received June 6, 2009. This work was supported by the Dean of Graduate Studies, College of Technology at Purdue University, West II. LITERATURE REVIEW Lafayette, Indiana. Logical access control systems in the healthcare industry C.R. Blomeke is a graduate research assistant in the Biometrics Standards, Performance and Assurance Laboratory, Department of Industrial have been primarily managed through the use of password Technology at Purdue University, West Lafayette, IN 47907 USA (e-mail: systems, but there is potential for the use of biometric blomekec@purdue.edu). technologies to help streamline access management systems. S.J. Elliott is Director of the Biometrics Standards, Performance and It is reported that 30% of all information technology (IT) Assurance Laboratory, and an Associate Professor in the Department of related help desk calls at healthcare organizations are Industrial Technology at Purdue University, West Lafayette, IN 47907 USA (e-mail: elliott@purdue.edu). password-related problems with a yearly maintenance fee of B. Senjaya is a graduate research assistant in the Biometrics Standards, $300 per user. According to [1] biometric technology would Performance and Assurance Laboratory, Department of Industrial help healthcare providers comply with the Health Insurance Technology at Purdue University, West Lafayette, IN 47907 USA (e-mail: Portability and Accountability Act (HIPAA). Within the bsenjaya@purdue.edu). G.T. Hales is a graduate research assistant in the Biometrics Standards, context of the HIPAA, biometrics falls within the Performance and Assurance Laboratory, Department of Industrial administrative simplification (AS) provisions by providing Technology at Purdue University, West Lafayette, IN 47907 USA (e-mail: unique identification. Whilst there are other types of ghales@purdue.edu). authentication technologies available such as passwords, personal identification numbers (PIN), card technologies and Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:24 from IEEE Xplore. Restrictions apply.
    • telephone call back, biometrics possesses several advantages quality and matching performance. to many of these other systems or by layering them, providing dual-factor authentication. Although the case for biometric III. METHODOLOGY technologies has been made in the traditional environment of The subjects of the healthcare population were recruited on PC access control, the question to ask is how biometric a voluntary basis from a local hospital. The data from technologies perform in the healthcare environment, healthcare workers were collected from nurses involved in specifically with front-line nursing staff. There have been a direct patient care (N = 30), and compared to individuals in number of studies that analyzed the image quality scores the general population (N = 30). The individuals completed between different age groups. In [2], the researchers the demographics survey and their skin characteristics of concluded that there was a correlation between image quality moisture, oiliness, and elasticity were collected using the and age regardless of the device or index finger. Another study Triplesense device from Moritex USA and subsequently by [3] specifically studied the impact of age on fingerprint recorded. The Triplesense device uses electrical capacitance recognition performance. The researchers used groupings for to measure the moisture level of the skin, an optical sensor to age of 18-25, 26-39, 40-62, and 62+ years old and concluded measure the sebum (oiliness) level, and a supersonic vibration that the means of the quality scores are different. sensor to measure the skin’s elasticity. The skin Representative ages from the healthcare population would characteristics were collected from the index finger of the cover the age groups of 26 and above. dominant hand. The skin characteristics were measured at the One study [2] analyzed fingerprint moisture, a factor that beginning of the study but were not measured again prior to could be influenced by age, and image quality for both interaction with the second capture device – the sensors were capacitance and optical sensors. The data indicated a used in sequence and not separated by time. The visit time statistically significant difference between the moisture with each participant lasted approximately 10 minutes, and it content between the young (18-25 years old) and the elderly was assumed that the skin characteristics would not change (62+ years old) for the right index finger using a commercially during this short interaction time. available capacitance sensor. According to [4], capacitance sensors react to different hydration (moisture) levels rather A. Optical Fingerprint Collection than to light changes as an optical sensor does. The moisture Individuals from the healthcare population presented their content of a person’s skin changes over time and it can be index fingers of the right hand followed by the left hand. affected by a number of factors. In the healthcare Three images of each finger were collected using the optical environment, repetitious use of sanitizing products or fingerprint sensor and saved for subsequent processing. procedures could reduce the amount of moisture in the hands Individuals were allowed 10 attempts for the system to of healthcare professionals. Another study [5] indicated that in successfully capture three images. The Aware image quality order to prevent “healthcare-associated infections”, software package was used to analyze image quality scores, compliance with specific hand hygiene practices is required in and Neurotechnology Verifinger, Version 6.0 was used to a healthcare environment. According to [6] frequent exposure collect the images and analyze the system performance. of skin to soap and water has significant effects on the structure and function of the stratum corneum barrier. The B. Swipe Capacitance Fingerprint Collection study denoted the majority of healthcare workers that Six images were collected from the right and left index complied with hand hygiene procedures during their shift fingers of healthcare population participants and saved for resulted in significant stratum corneum barrier damage that subsequent processing. Image quality scores were analyzed did not recover within 14 days as a normal person would. The using Aware image quality software package and effect of hand hygiene products with the base of emulsion Neurotechnology Verifinger, Version 6.0 was used to analyze cleanser, liquid soap, and alcohol handrinse were investigated the system performance. by [7, 8]. The emulsion cleanser reduced skin dryness while C. General Population datasets liquid soap increased skin dryness and redness and the alcohol handrinse resulted in decontamination of the skin surface. Two general datasets comprised the comparison general The literature contains very few evaluations of biometric population. The first dataset was created by selecting systems deployed into healthcare settings. In 2001, the individuals of a similar age to the healthcare population. An deployment of a fingerprint system was initialized to enroll attempt was made to minimize the age differences between the physicians and nurses for access to the electronic medical groups as much as possible due to the natural changes in skin records [9]. The white paper indicated that over 2,000 users corresponding to age. The elimination of age as a factor helps were enrolled into the system and would progress to over to determine the potential differences in image quality scores 5,000 users at the end of phase two and would be extended to and matching performance to be related to skin characteristics satellite facilities, yet there has not been a formal evaluation and the work environment rather than being compounded by that has been published of the performance of the system. age. The first general population dataset was collected on an While the literature offers some insight into potential optical sensor. The second population dataset was collected differences in skin characteristics in healthcare and general on a capacitance sensor. We call these two general populations, this study will seek to determine if these potential populations GPO and GPC. Both GPO and GPC consist of N=30. The average age of the healthcare population was 42.7 differences exist and what impact it has on fingerprint image Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:24 from IEEE Xplore. Restrictions apply.
    • years, and the average age of the general population was 28.0 relationship between the skin characteristic and the resulting years for the optical sensor and 36.6 years for the capacitance quality score. The scatter plots for each of the three skin sensor. The datasets for the optical and capacitance sensor characteristics by image quality score are shown in Fig. 1 and contain images from some but not all of the same individuals. Fig. 2. Inspection of the scatter plots do not reveal any clearly Therefore, no direct comparisons should be made across defined relationships between oiliness, moisture, or elasticity sensor types. and the image quality scores for neither the optical nor the capacitance sensor regardless of the population. The quality scores are evenly distributed across the range (0 – 100) of IV. RESULTS each skin characteristic. The results section is subdivided into sections representing the description of the image quality and skin characteristics and the analysis of the matching performance results. All comparisons were made between populations using the same sensing technology. A. Image Quality and Skin Characteristics Analysis The skin characteristics and output of the image quality software were measured on a 100 point scale, with 100 being high, and zero the lowest possible value. Table I. presents the median values of quality, oiliness and elasticity, and the mean value of the moisture content for each population and sensor based on the non-normal or normal distribution of the values respectively. The average image quality score over each individual’s fingers was used to calculate the overall average quality score for each population. There was no significant differences in image quality between left and right index Fig. 1. Optical sensor image quality versus oiliness, fingers for any given population and sensor, so the data was moisture, and elasticity (clockwise from top left) by aggregated. population (1 = Healthcare, 2 = General Optical - GPO). A Mann-Whitney U test was performed on the quality, oiliness, and elasticity data as a non-parametric method for comparing the medians of two independent populations (α=0.05). A two-sample t-test was performed on the moisture data. Statistical significant differences were found at the significance level α=0.05 between the healthcare and general populations for the variables oiliness and elasticity. The very low values of oiliness for the healthcare population can be attributed to the frequency of washing the hands with alcohol based cleansers or antibacterial soaps which strip the skin of its natural oils. The difference in skin elasticity measurements across populations could be a function of the device placement on the fingers or operator technique of using the device. Table I. Median quality, oiliness, and elasticity scores and mean moisture score are given by sensor and population. Fig. 2. Capacitance sensor image quality versus oiliness, moisture, and elasticity (clockwise from top left) by Quality Oiliness Elasticity Moisture population (1 = Healthcare, 2 = General Capacitance - GPC). Optical Healthcare 63.64 1.00 92.00 28.40 GPO 66.21 19.00 69.50 28.90 B. Matching Performance Analysis p-value 0.437 <0.001 0.001 0.910 The detection-error tradeoff curve is used to present the Capacitance performance of the dataset at various levels of a false accept Healthcare 73.54 1.00 92.00 28.40 GPC 73.58 13.50 71.50 34.33 rate (FAR) and its corresponding false reject rate (FRR). Fig. p-value 0.589 <0.001 0.002 0.187 3 shows the performance of the optical sensor datasets, with the healthcare population performance curve overlaid on the general population optical (GPO) curve. The overlapping The skin characteristics were plotted against the quality curves for all levels of FAR indicate that the two populations scores for each population to determine if there is a visible Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:24 from IEEE Xplore. Restrictions apply.
    • perform similarly. This is in contrast to the results shown in and general populations, but does not adversely affect Fig. 4, where the performance of each dataset is shown for the image quality scores and matching performance. capacitance sensor. While at high levels of FAR, the two From this small study, it is concluded that the differences populations have similar performance, the level of FRR is in skin characteristics between populations have minimal approximately 0.06% for the healthcare population for all effect on the fingerprint image quality score and matching levels of FAR less than 1%. performance. This is an interesting observation as it is in contrast to instructions given to participants in other deployments to add oil to the fingertips by rubbing their forehead to obtain a better quality image. Secondly, these observations conclude that the characteristics of the healthcare population are from the same distribution as those from the general population. This makes it possible to extrapolate data from the general population to the healthcare population for general skin characteristics and performance analysis. There is still a need to continue investigating the use of biometrics in the healthcare environment in terms of determining the most suitable modalities for the specialized departments and the incorporation of deployments with the requirements of HIPAA. The movement of patient identification towards Fig. 3. DET curve of matching performance of the the use of biometric identifiers prompts a variety of healthcare and general population (GPO) datasets from images research to be conducted to determine the appropriate collected with the optical fingerprint sensor. biometric technologies to be deployed in an environment where there is such variability in the traits that are collectable based on the severity of illness and or injury. REFERENCES [1] M. Kuperstein, “One and Done,” Health Management Technology, vol. 22, Dec. 2001, p. 22. [2] N. Sickler and S. Elliott, “An evaluation of fingerprint image quality across an elderly population vis-a-vis an 18-25 year old population,” Security Technology, 2005. CCST '05. 39th Annual 2005 International Carnahan Conference on, 2005, pp. 68-73. [3] S. Modi, S. Elliott, J. Whetsone, and Hakil Kim, “Impact of Age Groups on Fingerprint Recognition Performance,” Automatic Identification Advanced Technologies, 2007 IEEE Workshop on, 2007, pp. 19-23. [4] A. Bevilacqua. and A. Gherardi, “Age-related Skin Analysis by Capacitance Images,” Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK: Institute of Electrical and Electronics Engineers, 2004, pp. 703 - 706 [5] A.E. Aiello and E.L. Larson, “Causal inference: The case of hygiene and Fig. 4. DET curve of matching performance of the health,” American Journal of Infection Control, vol. 30, Dec. 2002, healthcare and general population (GPC) datasets from images pp. 503-511. collected with the capacitance fingerprint sensor. [6] M. Visscher, J. Canning, D. Said, R. Wickett, and P. Bondurant, “Effect of hand hygiene regimens on skin condition in health care workers,” American Journal of Infection Control, vol. 34, Dec. 2006, pp. S111-S123. V. CONCLUSIONS AND FUTURE WORK [7] J. Lauharanta, J. Ojajärvi, S. Sarna, and P. Mäkelä, “Prevention of The results of this study indicate that there are dryness and eczema of the hands of hospital staff by emulsion cleansing instead of washing with soap,” Journal of Hospital differences in the median scores for oiliness and elasticity Infection, vol. 17, Mar. 1991, pp.207-215. between the healthcare and general populations (optical [8] M. Winnefeld, M.A. Richard, M. Drancourt, and J.J. Grob, “Skin and capacitance) in this small study, although there is no tolerance and effectiveness of two hand decontamination procedures in defined relationship between image quality scores and the everyday hospital use,” Br J Dermatol. vol. 143, pp. 546-550. 2000. [9] IdentiPHI, Inc., “Biometric Security for St. Vincent Hospital and skin characteristics. The oiliness level in the healthcare Healthcare Centers,” 2007. population is assumed to be affected by the hygiene regimens that are followed, but this does not appear to have a large impact on fingerprint image quality scores and subsequent matching performance. Similarly, a difference in the skin’s elasticity was detected between the healthcare Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:24 from IEEE Xplore. Restrictions apply.