An Application of Automated Inkless Fingerprint Imaging Software in Fingerprint Collection and Pattern Analysis
1
Somsong Nanakorn1, Pongsakorn Poosankam2 and Art Nanakorn2 Department of Biology Faculty of Science 2Department of Computer Engineering Faculty of Engineering, Khon Kaen University 1
[email protected], 2
[email protected], 2
[email protected] Abstract
Fingerprint data collections of 684 Thai subjects using a transparent-adhesive tape technique compared with developed software namely an automated inkless fingerprint image which is able to scan fingertips and face images then arrange them in a portable document format prior to printing. Each fingerprint pattern was characterized as arch, radial loop, ulnar loop, and whorl (including double loop whorl, accidental whorl). An index of pattern complexity was constructed by subtracting the number of arches from the number of whorls. Analyses show that (1) finger pattern scores summed over ten fingers of the arch, radial loop, ulnar loop, and whorl are 0.32, 0.17, 4.89, and 4.62 respectively; (2) the index of pattern complexity is 4.30 (S.D.=3.61); (3) fingerprint patterns are statistically different among males and females on the left and right thumbs. The latter technique contributes electronic fingertips and face images which are rapid, large, and clear without dirt from ink/carbon.
1. Introduction Finger and palm prints are formed between the 11th and 24th weeks of fetal development and thereafter remain unchanged. Each individual has a unique finger and palm prints configuration largely determined by the genetic profiles of the parents [1] and the intrauterine environment [2]. Three types of measures are commonly used: a qualitative analysis of dividing fingerprint patterns into arches, radial loops, ulnar loops and whorls; quantitative analysis of ridges on fingers and palm prints; and measures of asymmetry between left and right hands [3]. A live-scan collection of inkless fingertip images should be enhanced for fingerprint research due to conventional methods i.e. an ink-print, transparent-adhesive tape techniques are slower and cumbersome [4]. The transparent-adhesive tape method which is commonly used in fingerprint research needs several materials such as, transparent-
adhesive tape, a black pencil for fingerprints collection and a magnifying lens for fingerprint patterns analysis [5, 6]. The ink-print technique used by police departments needs an expensively special ink, and rollers, which is messy and dirty. Moreover, both methods are time-consuming. A live-scan with software namely an automated inkless fingerprint image had been developed by Nanakorn et al. [7] which is able to scan fingertips and face images then arrange them in a portable document format (pdf) prior to printing. The present study aimed at collecting fingerprints using two techniques; the transparentadhesive tape technique and the live-scan inkless technique for the same subjects and analyzing those fingerprint patterns.
2. Materials and method Subjects were Thai students who had studied in the introductory genetics laboratory in the topic “polygenic inheritance” in the undergraduate course. 684 subjects were taught to make their own fingerprinting by using a conventional method i.e. the transparent-adhesive tape technique [5, 6]. Their fingerprints were collected a second time using the live-scan inkless fingerprint technique [7, 8]. Briefly, touching each fingertip on a touch pad of the fingerprint sensor (IT WORKS Co., Ltd.) starting from the left hand; thumb, index, middle, ring, little finger, followed by the right hand of little, ring, middle, index, and thumb (Fig.1). These fingertip images and subjects’ faces are stored into a data base as a pdf prior to printing (Fig.2). Fingerprint patterns obtained from both techniques were identified by a fingerprint researcher (S.N.). Each fingerprint pattern was characterized as arch, radial loop, ulnar loop, and whorl (including double loop whorl, accidental whorl), as shown in Fig. 3. Reliability of the live-scan inkless fingerprint technique was measured by comparing the results of fingerprint patterns classified by the first author with the conventional method; the transparentadhesive tape technique. For data analyses, an index of pattern complexity was constructed by subtracting the
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number of arches’ pattern from the number of whorls. Gender differences of fingerprint patterns on each fingertip were tested using the Chi-square statistic. This research protocol had been reviewed and approved by the Khon Kaen University Ethics Committee for Human Research.
3. Results Reliability assessment Comparison of fingerprint patterns of these two techniques shows that the Kappa statistic exceeded 0.94 indicating that the inter-rater reliability of this live-scan technique for fingerprint pattern configuration is satisfactory. Comparative images of the fingerprint patterns obtained from the transparentadhesive tape technique and the live-scan inkless technique are shown in Figure 4. The image of fingerprint patterns obtained from the live-scan inkless fingerprint technique is bigger and much clearer compared to that of the transparent-adhesive tape method.
Fingerprint pattern scores and index of pattern complexity Fingerprint pattern scores over 10 fingers of the 684 Thai subjects are 0.32 for the arches pattern, 0.17 for radial loops, 4.89 for ulnar loops and 4.62 for whorls. The data are consistent with other studies among Thai subjects [6, 9] and other populations [9, 10] (Table 1). An index of pattern complexity was constructed by subtracting the number of arches from the number of whorls resulting in its index ranges from minus 10 to plus 10. The present study reveals a mean pattern complexity of 4.30 (S.D.=3.61) compared to 3.88 (S.D.=3.76) of the English sample [10].
Fingerprint patterns’ differences by gender There were statistically significant differences of the fingerprint patterns on the left thumb among males and females. The percentages of fingerprint patterns: arches, radial loops, ulnar loops, and whorls are 3.07, 0.61, 36.5, and 59.82 respectively on the left thumbs of males, while those of females are 5.59. 0.0, 43.02, and 51.4 respectively. (χ2=8.66, d.f.=3, p=0.034, Fig. 5). Similarly, fingerprint patterns on the right thumbs of males: arches, radial loops, ulnar loops, and whorls are significantly different from females; 1.84%, 0.31%, 30.37%, and 67.48% vs 4.6%, 0.92%, 44.48%, and 59.82% respectively. (χ2=13.57, d.f.=3, p=0.004). The
results are comparable to the previous studies among Thais [6, 9].
4. Discussion The automated inkless fingerprint imaging software which was developed by Nanakorn et al. [7, 8] had been applied to the live-scan collection of fingerprints in the fingerprint research instead of using a conventional method such as ink-print or transparentadhesive tape technique or commercial print-kits. It contributes a clear and large image of fingerprint area without dirt of ink or carbon which is helpful in classification of patterns by the naked eye. However, the live-scan technique hardly scans all the pattern area of a very large fingerprint especially the whorl type which has two delta points at the edges of the pattern. This caused by a flat shape of the finger sensor’s touch pad. Figure 6, 7 show missing delta points of the whorl fingerprint images obtained by the live-scan method compared to the transparent-adhesive tape technique’s image which is smaller but covers all the pattern area. A trained researcher for understanding a fingerprint pattern’s characteristic can solve this technical problem. Thus, using the live-scan inkless fingerprint technique for data collection of fingerprint research should be enhanced as it is easily accessible, rapid, not messy, and it is stored in an electronic pdf as well and is clearly seen by the naked eye which results in a practice way to classify the pattern. Finger and palm prints researches have become more useful in preventive medicine according to abnormalities of finger and palm prints as lifelong markers of several kinds of genetic diseases such as schizophrenia, intellectual disability, diabetes, and cancer [11-15]. Future research should attempt to do the following: (1) a development of a software package that directly classifies various types of fingerprint patterns and is able to count the ridges of fingerprints obtained from the live-scan technique; and (2) a construction of a fingerprint sensor module that is able to scan all surfaces of fingertips to obtain full images of fingerprint pattern area.
5. Acknowledgements The authors would like to thank the undergraduate students of the Khon Kaen University, Thailand for good cooperation in practice during laboratory study. Professor Dr. Brian Sheehan is also appreciated for his English checking. This study was partly supported by the Applied Taxonomic Research Center, Khon Kaen University grant ATRC_R4903.
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Figure 3. Three basic fingerprint patterns: (a) arch; (b) loop has one delta, ulnar loop has ridges flow to the ulnar margin (little finger), radial loop has the ridges open to the radial margin (thumb); and (c) whorl has two delta, [obtained from live-scan technique; scan resolution=200 dpi]
Figure 1. An outlook of fingertips printing using the live-scan inkless fingerprint technique
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Figure 4. Comparison of the fingerprint pattern images [scan resolution=200 dpi] obtained from two techniques (a) the transparentadhesive tape technique and (b) the live-scan inkless fingerprint technique 300 250 200
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Figure 2. A pdf printout of fingertips printing using the live-scan inkless fingerprint technique
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Figure 5. Number of fingerprint patterns on the left fingertips [thumb (L1), index (L2), middle (L3), ring (L4), Little (L5)] by sex
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Figure 6. Missing delta point of the large finger pattern image obtained from the live-scan technique (a) compared to the same finger pattern image collected by the transparentadhesive tape technique (b) [scan resolution=200 dpi]
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Figure 7. Missing delta points of the large finger pattern images obtained from the livescan technique (a) whorl (b) double loop whorl [scan resolution=200 dpi] Table 1. Mean scores of finger patterns summed over 10 fingers fingerprint pattern/ arches radial ulnar whorls Subjects (n) loops loops 1 Thais (684) 0.32 0.17 4.89 4.62 2 Thais (865) 0.26 0.40 4.85 4.49 3 Thais (11434) 0.24 0.32 4.76 4.68 4 English (26) 0.27 0.31 5.27 4.15 5 Japanese (6500) 0.25 0.16 5.18* 4.41 5 Chinese (5000) 0.42 NA 5.71* 3.87 5 Korean (1000) 0.24 NA 5.27* 4.49 1 2 present study Nanakorn et al., 2006 3 Visonkosol, 4 1980 Langsley et al., 2005 5 cited in Visonkosol, 1980 * included radial loops NA not available
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[2] W.J. Babler, “Prenatal selection and dermatoglyphic patterns”, American J Physical Anthropology, Blackwell Science Ltd, Oxford, 1978, 48, pp. 21-28. [3] Cummins, H., and C. Midlo, Fingerprints, palms and soles, Blakinston, New York, 1943. [4] H.S. Kahn, “Enhanced collection of fingerprints and ridge counting”, American J Human Biology, Wiley-Liss, Inc., USA., 2005, 17, pp. 383. [5] Mertens, T.R., and R.L. Hammersmith, Genetics: laboratory investigations, Prentice Hall Upper Saddle River, New Jersey, 1998. [6] S. Nanakorn, P. Mongconthawornchai, K. Thepsuthammarat, and K. Chusilp, “Fingerprint patterns and ridge counts of a sample of Thai population”, Science Journal, Science Association of Thailand under the Royal Patronage, Bangkok, 2006, 60(6), pp. 468-474. (in Thai) [7] S. Nanakorn, P. Poosankam, and P. Mongconthawornchai, “Imaging software for automated inkless fingerprinting”, Proceedings, The first international conference on science and technology for sustainable development of the greater Mekong sub-region, Khon Kaen, Thailand, 15-16 August 2006, pp. 128. [8] S. Nanakorn, P. Poosankam, and P. Mongconthawornchai, “Automated inkless fingerprinting imaging software for fingerprint research”, J Med Assoc Thai, Medical Association of Thailand, Bangkok, 2007, 90 (in press). [9] Visonkosol, V., “The fingerprint of Thai population: a qualitative and quantitative analysis”, Thesis, Mahidol University, Bangkok, 1980. [10] N. Langsley, P. Miller, S.M. Lawric, A. Macintosh, E.C. Johnstone, and M. Byrne, “Dermatoglyphics and schizophrenia: findings from the Edinbergh high risk study”, Schizophrenia Research, Elsevier B.V., Amsterdam, 2005, 74, pp. 122-124. [11] M.T. Avila, J. Sherr, L.E. Valentine, T.A. Blaxton, and G.K. Thaker, “Neurodevelopmental interactions conferring risk for schizophrenia: a study of dermatoglyphic markers in patients and relatives”, Schizophrenia Bulletin, ProQuest, Washington, 2003, 29(3), pp. 595-602. [12] A. Rosa, L. Fananas, H.S. Bracha, E.F. Torrey, and J. van Os, “Congenital dermatoglyphic malformations and psychosis: a twin study”, American J Psychiatry, ProQuest, Washington, 2000, 157(9), pp. 1511-1513. [13] A. Rosa, B. Gutierrez, A. Guerra, B. Arias, and L. Fananas, “Dermatoglyphics and abnormal palmar flexion creases as markers of early prenatal stress in children with idiopathic intellectual disability”, J Intellectual Disability Research”, Blackwell Science Ltd, Oxford, 2001, 45(5), pp. 416-423. [14] H.S. Kahn, R. Ravindranath, R. Valdez, and K.M.V. Narayan, “Fingerprint ridge count difference between adjacent fingertips (dR45) predicts upper body tissue distribution: evidence for early gestational programming”, American J Epidemiology, The John Hopkins University School of Hygiene and Public Health, 2001, 153(4), pp. 338344. [15] Y. Zhou, Y. Zeng, Lizhen, and W. Hu, “Application and development of palm print research”, Technology and Health Care, IOS Press, Amsterdam, 2002, 10, pp. 383-390.
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