Impact of Image Quality on Performance: Comparison of Young and Elderly Fingerprints

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1 Impact of Image Quality on Performance: Comparison of Young and Elderly Fingerprints Shimon K. Modi and Dr. Stephen J. Elliott Department of Industrial Technology Purdue University West Lafayette, Indiana , U.S.A. {shimon, Abstract: Performance of fingerprint recognition systems is heavily influenced by the quality of fingerprints provided by the user. Image quality analysis is traditionally performed using local and global structures of fingerprint images like ridge flow, analysis of ridge-valley structures, contrast ratios etc. With large scale deployment of fingerprint recognition in systems like US VISIT program, image quality issues of fingerprint images from extreme age groups becomes even a more important issue. The impact of image quality on performance of fingerprint recognition systems should be a positive one i.e. higher image quality should lead to better overall performance of the system, and removal of lower quality images should improve performance of the system. This research study studied the impact of fingerprint image quality of two different age groups: 18-25, and 62 and above on overall performance using two different matchers. The difference in image quality between the two age groups was analyzed, and then the impact of image quality on performance of fingerprint matchers between the two groups was analyzed. Image quality analysis was performed using NFIQ which is part of NIST Fingerprint Image Software (NFIS). Neurotechnologija VeriFinger and bozorth3 (NFIS) matchers were used to assess overall performance. For the purposes of the research study, overall performance was measured using False Non Matches. Keywords: biometric sample quality, fingerprint image quality, young and elderly fingerprints, quality scores, performance rates. 1. Introduction Fingerprint recognition is the most widely used biometrics in government and commercial applications. Its uses spread across the security spectrum from single sign on applications to border control applications. With such a wide variety of uses for the technology, the demographics and environment conditions that it is used in are just as diverse. The quality of fingerprints provided by the users will not conform to a particular set of characteristics. Non-uniform and irreproducible contact between the fingerprint and the platen of a fingerprint sensor can result in an image with poor utility quality. Non-uniform contact can result when the presented fingerprint is too dry, or too wet. Irreproducible contact occurs when the fingerprint ridges are semi-permanently or permanently changed due to manual labor, injuries, disease, scars or other circumstances such as loose skin [1]. Contact issues can affect the sample provided to the fingerprint sensor when an elderly user presents a fingerprint to the fingerprint device. Due to effect of ageing, the skin becomes drier, the skin sags from loss of collagen, and the skin becomes thinner and loses fat as a direct result of elastin fibers. All of these decrease the firmness of the skin, which affects the ability of the sensor to capture a high quality image [2]. The skin of elderly individuals is likely to have incurred some sort of damage to the skin over life of the individual. Medical conditions like arthritis affect the ability of the user to interact with the fingerprint sensor. All of these factors affect the quality of the sample provided to the fingerprint sensor. Proceedings of the 6th International Conference on Recent Advances in Soft Computing (RASC 2006), K. Sirlantzis (Ed.), pp , 2006

2 Such inconsistencies make it essential to establish the image quality of the fingerprint being presented to the sensor. The issues related to fingerprint quality have been discussed widely in the biometrics literature [3-7]. The usefulness of quality metrics is universally acknowledged, but there are no standard means of measuring quality of fingerprint samples. There is a current standardization effort in ISO that is trying to tackle this problem. The current draft divides the overall quality metric into three components: Character, Fidelity, and Utility. Character represents inherent features of the source from which the biometric sample is derived. Fidelity represents how closely the samples from the same individual match. Utility represents the contribution of a biometric sample to the overall performance of a system. The utility of a sample is affected by the character and fidelity of the sample, and performance of a system is affected by the utility of a biometric sample. Low quality images affect the feature extraction process, and results in extraction of inaccurate and spurious features. The overall utility of the fingerprint image is reduced because it affects the performance of the entire fingerprint recognition system. This paper gives an overview of the research study conducted to study the difference in image quality of fingerprint samples between a young dataset (18-25 years) and elderly dataset (62 years and above). The focus of the research was to better understand the effects of minutiae count and image quality, and how extreme populations affect the overall performance of the system. 2. Background A considerable amount of effort has been put towards addressing the problem of assessing fingerprint image quality. In previous work done by Bolle et al. they used ratio of directional area to non directional area as a quality measure [8]. Lim, Jian, and Yau describe a quality analysis that examined local and global structures of the fingerprint image [9]. Comparison of ridges and valleys, and local orientation certainty were examined for local structures. Continuity of ridge orientation and variation in ridge to valley ratio were examined for global structures. Jain, Chen and Dass describe two different quality indices for fingerprint images quality [10]. The first index measured the energy concentration in the frequency domain as a global feature, and the second index measured spatial coherence as a local feature. Yao, Pankati and Hass developed a fingerprint image quality algorithm that is based on computing the ratio of total weights of directional blocks to the total weights for each of the blocks in the foreground [11]. The foreground blocks are separated from background regions that may have a faint residue left over from previously captured prints. The directional prominence for each of the foreground blocks is computed and each foreground block is given a relative weight depending on its distance from the centroid of the foreground. 3. Data Set This research study analyzed fingerprint images from two different age groups: years old and 62 years and above. The young age group (18-25) consisted of 79 subjects, and the elderly group (62 years and above) consisted of 60 subjects. The fingerprint images were collected using DigitalPersona U.are.U optical fingerprint sensor. 4 placements from the left index finger and 4 placements from the right index finger were collected. There were a total of 480 finger placements for the elderly group, and 632 finger placements for the young group. 4. Methodology and Results Feature extraction is completely dependent on the quality of fingerprint images provided to the system. As mentioned earlier, different environmental and ageing factors affect the quality of the images provided to the fingerprint sensor. The average number of minutiae counts for the young group and elderly group was computed. The average number of minutiae points for the young group was

3 54.8 and average number of minutiae points for the elderly group was Figure 1 and figure 2 show the histogram of the minutiae count for the young and elderly group respectively. Previous research in the field has shown that good quality fingerprints have about minutiae points [14], and the higher number of average minutiae points for the elderly dataset could indicate that spurious minutiae were included in the feature extraction process. 40 Histogram (with Normal Curve) of min_count_elderly Mean StDev N Frequency min_count_elderly Figure 1.Minutiae histogram young dataset Figure 2. Minutiae histogram elderly dataset Using the nfiq process which is a part of National Institute of Standards and Technology (NIST) Fingerprint Image Software 2 (NFIS2), the quality scores for the fingerprints from the two different age groups was computed. All quality scores are computed in the range of 1-5, where 1 indicates best quality possible and 5 indicates worst quality possible. The average quality score for the young group was 1.8 and the average quality score for the elderly group was 4. Figure 3 and figure 4 shows the histogram of the quality scores for the young and elderly dataset respectively. Figure 3. Quality score histogram young dataset Figure 4. Quality score histogram elderly dataset The next step in the research study examined the effect of image quality on the number of false non matches for both the datasets. The fingerprint image may not be visually of good quality, but it might still provide enough features to not have a significant effect on the performance of the system. In order to study this effect, the number of false non matches was first calculated for the two different age groups. Then 20 of the fingerprint images with the lowest score were removed from the two age groups and the number of false non matches was recalculated. This step was repeated three more times with the intention of identifying a relation between image quality and number of false non matches. Two different matchers, Neurotechnologija VeriFinger and bozorth3 (NFIS2), were used. For the bozorth3 matcher a threshold of 40 was used in accordance with previous tests conducted by NIST [12]. The elderly dataset had a total of 585 matching operations, and the young dataset had a total of

4 948 matching operations. Figure 5 and figure 6 show the effect of removing low quality images on the number of false non matches. Young Dataset False non matches NIST Matcher VeriFinger 2 0 All Iteration 1 Iteration 2 Iteration 3 Iteration 4 Removal of low quality images Figure 5. Effect of removal of low quality images on false non matches for young dataset Elderly Dataset False non matches NIST Matcher VeriFinger 0 All Iteration 1 Iteration 2 Iteration 3 Iteration 4 Removal of low quality images Figure 6. Effect of removal of low quality images on false non matches for elderly dataset The number of false non matches decreased for both the datasets as the low quality images were removed. One potential cause for the extremely high number of false non matches could be the large number of low quality images, as seen in figure 4. Images from the young dataset were mostly of high quality, which resulted in the low number of false non matches for the young dataset. Figure 7. ROC for young dataset Figure 8. ROC for elderly dataset

5 Receiver Operating Characteristic (ROC) curves were computed for the young dataset and the elderly dataset using Neurotechnologija VeriFinger. Figure 7 and figure 8 show the two ROC curves. The young dataset consisted of a large number of high quality fingerprint images and the elderly dataset consisted of a large number of low quality fingerprint images. Reliable minutiae detection and extraction is extremely difficult in low quality fingerprint images which directly impacts performance of the fingerprint recognition system [13], as seen in figure 8. The young dataset consisted mostly of high quality images which can be one of the reasons for the low levels of false accept rates and false reject rates seen in the ROC curve. This result of high level of performance from good fingerprint images followed as expected. 5. Conclusions and Future Work Minutiae analysis on the young and the elderly fingerprint datasets showed that the elderly had a higher number of minutiae points. The image quality of the fingerprints for the elderly dataset was of lower quality even though they were collected in a controlled environment. Wear and tear on fingerprints and non-uniform contact with the sensor can produce spurious minutiae and images with a high level of background noise. The analysis of the elderly fingerprint dataset shows that image enhancement and feature extraction algorithms need to take into consideration the characteristic of elderly fingerprints. The results show that the feature extraction component of the matching algorithm works sufficiently well for fingerprint images from the young population, but the performance degrades significantly when used on the elderly population. The difference in quality of images between the young and elderly dataset indicates the need to accommodate for fingerprints from an elderly population. Removal of lower quality images from both the datasets showed that the number of false non matches decreased which shows that performance of the system can be significantly improved by removing images of lower quality. For a large scale database that includes subjects from a diverse age group, the impact of fingerprint image quality on the performance of the system will be significant. Large scale implementations of fingerprint recognition systems like the US VISIT program and disbursements of welfare payments will have users that belong to the elderly population. For these systems to achieve desired performance levels, fingerprint image quality issues arising from the elderly population needs to be addressed. More research needs to be conducted in the field of image enhancement, specifically related to extreme populations, in order to reduce the impact of low quality fingerprints on the performance of the system. Research into improving the physical interaction between the subject and the sensor needs to be conducted to explore different possibilities of improving image quality. Consistent determination of fingerprint image quality can greatly enhance performance of matchers, and the overall performance of the system. Understanding how fingerprints from different age populations contribute to system performance is extremely important to improve accuracy. References [1] A. Jain, L. Hong, S. Pankanti, and R. Bolle (1997). An Identity-Authentication System Using Fingerprints. In Proceedings of the IEEE, vol. 85, pages 1,365-1,388, September [2] American Academy of Dermatology. In Mature Skin, vol. 2002, [3] S. Der, P. Phillips, and P. Rauss (1996). FERET (Face Recognition Technology) Recognition Algorithm Development and Test Report, U.S. Army Research Laboratory ARL-TR-995, October [4] G. Behrens (2002). Assessing the Stability Problems of Biometric Features. In International Biometrics Conference and Exhibition, Amsterdam, NL, March 2002.

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