A review of ridge counting in dermatoglyphics

Pattern Recogmtton Vol 16 No 1 pp 1 4 1983 Printed in Great Bl'lhlln

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A REVIEW OF RIDGE COUNTING IN DERMATOGLYPHICS* WEI-CHUNG Lm and

RICttARD C

DUBESt

Computer Science Department, Michigan State University, East Lansing, MI 48824, U S A (Received 16 June 1981, m revised form 8 December 1981, recetved for pubhcatlon 5 February 1982) A b s t r a c t - - T h i s paper reviews the problem of counting ridges m a digitized fingerprint and examines

an interactive software system that relieves the tedium of v~sualinspection and standardizes the counting procedure The software system Includes digitizer, preprocessor and counter subsystems The preprocessor smoothes and thresholds the image The counter defines an appropriate window around thc line bctween two user-selected points, eliminates artifacts and counts runs of zero p~xels A prchmmary experiment with a counter based on the Hough transform is also briefly discussed

Dermatoglyphlcs

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Pattern recognmon

Image processing

INTRODUCTION

Hough transform

the hentabdlty of finger and palm prints Since then, fingerprints have been used as the basic tool for identification of criminals m law enforcement (forensic science), for security clearances m the armed services and for anthropological and medical studies In the past decades, the need for automatic fingerprint processors along with the progress m pattern recogration and image processing have led to much research This paper summarizes approaches to automatic ridge counting and exammes a technique for measurmg finger rldge-counts with a computerbased image processing system

Dermatoglyphlcs is the study of the structures embossed m the ridged skin on human fingertips, palms and soles, which are determined by the genetic constitution and fetal environment of the indwldual (1) The identification of significant dermatoglyphlc traits is valuable m medical prognosis, i e , the |dentificat~on of a predisposition towards various geneticallyinduced syndromes and disease entrees, as well as m medical diagnosis Dermatoglyphic abnormahhes may result from chromosomal aberration or they may be assocmted with a condmon that results from an environmental mfect~on m utero, such as rubella (2-4) The most familiar dermatoglyphlc structures are the fingerprint patterns, or the loops, whorls and arches etched in the ridged skin on the fingertips which are generally assumed to be umque to each individual The reference points on a fingerprint pattern are called the core, or center of the pattern, and the trlradius. A fingerprint pattern can have no tnradms (arches), one (loop), two (whorl) or more triradn A single triradms occurs at the confluence of three fields of parallel ridges, as demonstrated m Fig 1 A finger ridge-count is the number of ridges crossmg an imaginary hne between the tnradms and core of a fingertip pattern and is one of the most ~mportant dermatoglyphic measurements. Fig 1 shows a typical loop pattern in a digitized fingerprint image The usefulness of fingerprints for personal identification was recognized long ago, followmg the work of Galton (5) Galton not only pointed out that the mmutme of fingerprints remain unchanged throughout the life of an individual, but also observed

2 BACKGROUND

Methodologically speaking, dermatoglyphtc studies can be dichotomized into quahtatlve studies and quanhtatwe studies Qualitative studies observe the types of dermatoglyphlcs traits, such as loop and whorls, at various anatomical locations Quantitative studies involve palm measurements, ridge counts and other objectively defined quantities The discovery of the mode of inheritance for some quantitative traits (6) suggests that dermatoglyphic studies for diagnostic purposes should be quantltatlye Although the counting of ridges seems to be straightforward, the presence of extensive Irregularities and noise m a fingerprint have made it a difficult problem Epidermal ridges can be compared to the ribs of corduroy fabric, but they differ m irregularities of direction, discontinuities and branches Figure 1 shows some characteristic minutiae of individual ridges which complicate the definition of ridge for counting purposes The ridge counting system proposed here Is designed to minimize user involvement and computation time

*Research supported by NSF Grant ECS-8007106 tAddress all correspondence to Dr Dubes p p 1~ 1 . A

Ridge counting

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Dermatoglyphlcs studies in the pattern recognmon hterature can be separated into two categories The first category uses dermatoglyphtcs for identification of mdwlduals This category mcludes minutiae detection, (7-12) pattern reDstration and matching, (13-16) pattern description and reconstruction, 07 ]8) topological representation, (19-2°) latent print enhancement (1) and fingerprmt pattern classlficauon . . . . . The second category applies dermatoglyphlcs for diagnostic purposes Although the literature abounds with dermatoglyphlc signs for various syndromes and disease entities, very little has been done in applying pattern recognition methodologies to dermatoglyphlc studies for medical diagnostic purposcs (29)Our paper reports an automatic system for counting ridges in the fingerprints, a first step towards the automatic measurement of dermatoglyphlc traits matoglyphic tra~ts The proposed interactwe system, shown m F~g 2, consists of three basic subsystems dtgmzer, preprocessor and counter The dtDtlzer scans and converts the fingerprint into numbers representing light intensities. The preprocessor cleans the picture and the counter determmes the number of ridges that cross a user-defined hne m the fingerprint The user selects two points 0 e , triradlus and core) to define the line, by markmg them mteractwely with a joyst~ck cursor. Such a system for automatically countmg ridges provides an objective means for collecting data It also relieves the tedmm of visual examma-

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tion, thus ehmmatmg operator errors Although general agreement exists on the meaning of ridge count, a specific criterion ts needed to judge whether certain mmutme should be counted as ridges, especially in the neighborhood of the triradms. The proposed system eliminates arbitrary judgements and imphcitly defines the concept of ridge count

3. METHOD

A fingerprint ridge-count is the number of ridges which intersect or touch a straight line drawn from the central point of a tnradms to the center or core of a hngerprlnt pattern Ridges generally cross this hne at right angles The tnrad]al point is not included m the count nor is the last ridge if it ~s the central island of a pattern Two ridges, which result from a bifurcation and cross the straight line, are both counted but ridges which lie close to the stnaght line without touching it are excluded Interstltml lines are not counted (1) A dlgmzed fingerprint is often "noisy" Irregularmes m the digmzat~on process and m the fingerprint itself are caused by several factors (1) Fingerprint imperfections such as ridge gaps, usually caused by skmfolds, or injuries to the skin, such as cuts, burns and abrasions, affect the alignment of ridges and may even result in the destruction of some ridges ,(6)

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A I~.v~cwol ridge counting m dt.t matoglyphlcs (2) Minutiae details of the print itself, such as pore holes on the ridges, can cause false gaps or breaks in the l ltlgc lines, (3) Contiguous and smeared ridges may be introduced by improper recording, (4) Distortion introduced by the digitization operat~on can affect the accuracy of the count The performance of the automatic ridge-counting system depends on the degree of irregularities, or "noise", in the digitized picture Some preprocessmg o p e r a t i o n s f o r n o i s e e h m l n a t l o n a r e necessary before the counting process itself

3 1 PrtT~rotes~mg Rao et al (22) have proposed an automatic fingerprint classlhcatton system which includes a ridgecounting subsystem Because it is a multlfunctlonal system, the preprocessmg includes threshold selection, bmanzatloi~, throning and nldny nolse-removdl operations Some preprocessmg techniques are proposed to remove the noise from a fingerprint nnage Rao (23) devised an ad-hoc smoothing algorithm to hll the Isolated holes, remove the noise and bridge the gaps He then applied Deutch's thinrang algorlthm¢ 30) to reduce the thickness of the ridges to one plxel Chlralo et al (21) presented an adaptwe techmque for providing effective enhancement ot latent fingerprint Because the objective of our system is to count the ridges, images need not be as clean as those needed for automatic fingerprint recognition and classification The preprocessor includes the operations of smoothing and blnarizatlon The smoothing algorithm replaces the gray level of each plxel by the average gray level of plxels surrounding it Our system uses a 5 × 5 block averaging operation which removes "salt-and-pepper" noise, fills small isolated holes in the ridges and bridges small gaps The smoothed image Is thresholded into a binary image to facilitate counting. The threshold is selected to make the number of black plxels approximately equal to the number of white plxels, which produces good binary ~mages with little computation This preprocessmg algorithm IS the same for every image, thus ehminatmg the need for ad-hoc selection of thresholds Figure 3 shows an original digitized hngerprnlt lm,lge (480 x 641) pixels) and the results o! the prcproccsslng

3 2 Rulge counting The counting algorithm consists of the following three steps applied to a preprocessed image (I) window selection, (2) artifact ehmlnatlon, (3) counting runs of zeros A window is a narrow box drawn around the hne between core and tnradius, arranged so that the ridges are approximately parallel thick

3

straight lines inside the window Depending on the rchltwe positions of the trlradlus and core selected by the u s e r , 01112 ol | h e t h r e e case,, indic,fled m Fig 4 occurs These windows Include as much of the local reformation as possible that is relevant to the ridgecount and exclude irrelevent and nusleadlng material The window shape is easy to implement and is small to reduce computation time, yet provides an accurate ridge count The width of the window (parameter r In Fig 4) depends on the magmhcatlon of the print or on the thickness of the ridges and the distance between the ridges A typical example of a case 1 window is shown in Fig 5a A window leads to a binary matrix of size r x c A partial printout of a typical window IS shown In Fig 5b The final step is to count the number of runs of zeros in each row of the window A row is a line of plxels parallel to the line between trlradlus and core inside the window Each row yields a run count so that a histogram of run counts is obtained The mode of this histogram estimates the ridge count For example, most of the rows in Fig 5b yield run counts of 4, so the mode will be 4 The actual counting of runs is preceded by a onedimensional smoothing operation on each row of the window to eliminate very short runs of () and 1 pixels, such as those m rows 12 and 13 of Fig 5b These short runs represent artifacts of the digital operations and interstitial ridges This type of smoothing is more computatlonally efficient than smoothing on the original digital image All runs of s~ze 2 or less were eliminated m our experiments before counting

4 EXPERIMENTAL RESULTb

The automatic fingerprint ridge counting system was Implemented in F O R T R A N IV and run on the PDP/II-34 minicomputer and Spatial Data Image Processing System in the Pattern Recognition and Image Processing Laboratory at Michigan State University The original fingerprint images were magnified approximately 20 times Preprocesslng time was about 2 minutes excluding I/0 The windowrag, artltacts eliminating and counting operations altogether take from 20 to 30 seconds depending on the horizontal distance between the two end points Our experiments used the following parameters (1) the width of the window (r) is 21 plxels in case 1, (2) minimum allowable run length during artifact elimination IS 2, (3) magnification factor IS about 23, (4) Window selection parameters Case 1--[:tl - x 2 [ > 40 plxels, Case 3--[~1 - a 2 I < 20 plxels

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The counting procedure was validated by colnparmg its estimates with direct observations In 21 out of 23 fingerprints tested, only two counts differed by one or more One of the two incorrect counts was due to a malformed print The other error was caused by holes m the ridges which were too big to be filled by the smoothing operation 5 EXPERIMENT WI r l l IIOU(,lt I RANSFORM

An alternatwe ridge counting schemc can be ba,,ed on the Hough transform, I~t-a~l a procedure for detectmg and findmg strmght lines, parabolas and other curves that can be specified by a small number of parameters within a noisy image Scveral varhttlons and extensions of tlough-hke transform have been devised (33) We attempted to use Duda and Hart's (~2) "normal paramctcr~z,xt,on" of the Hough transform to detect the ridges After the preprocessing. we first applied the Hlldltch thinning algorithm (34) to obtam the thinned fingerprint image as shown m Fig 6 Smce the throned ridge curves in the preselected small window approximate straight lines, we then transformed each ndgc plxcl (~. y) m the prcsclcctcd window mto a smusoldal curvc in the 0 p plane defined by

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mdlcate cohnear ridge plxels that may be fitted by a strmght hne with appropriate (0,p) parameters The number of ceils having large counts estlmates the ridge count Our prehmmary experiments demonstrated the following drawbacks (1) Some undesirable I,nes caused by noise also have numerous cohncar plxcls and result m large counts m the correspond,ng cells, (2) The threshold value on cell counts needed to differentiate ridges from other noise hnes is difficult to determine. (3) Since the distance between successive ridges is not constant, it is difficult to find an appropriate (constant) quantlzatlon mterval on #, (4) The thinning operation as well as the Hough transform reqmres excesswe computation hme In addition, since the ridge curves on which the Hough transform is applied are obtained from the output of the th,nnmg algorithm, the Information of the gradient directions of the ridge plxels which can be used to narrow the 0-range m the transformed space is not available The mformat~on can be obtained by an edge detection algorithm (e g , the Hueckel edge detector(3S)), but this will needlessly comphcate the preprocessmg

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6. CONCLUDING REMARKS

The 0 and p axes are quantmzed (e g 0axis with increment of 10 and p with increment of 5) and the O-p plane is divided into a large number of cells. After the above transformation w~th the origin of the 0-p coordinate set m one of the two end pomts, the sampled smusoldal curve which results from a ridge plxel falls into several of these cells A count for each cell of the O-p plane is maintained Large cell counts correspond to intersecting smusoldal curves which

We have reviewed the problem of fingerprint ridge counting and examined a specific procedure designed to mtmmlze user involvement and computation t~me The software system mcludes dlgmzer, preprocessor and counter subsystems. The preprocessor smoothes and thresholds the image The counter determmes an appropriate window around the line between two user-selected points, eliminates artifacts and counts the clusters of zero plxels The system shows satlsfac-

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A review ol ridge countmg,m dcrmatoglyphlts tory results as long as we magnify the ortgmat lingerp r i n t ~mage at least 20 t~mes a n d has c o m p u t a t i o n a l a d v a n t a g e s o v e r a c o u n t e r b a s e d on the H o u g h transform This system was d e s i g n e d only for fingerprmt ~mages F u r t h e r investigations are n e e d e d m extending the work to include the c o u n t i n g of ridges m palms T h e p r o c e d u r e m a y be e x p a n d e d to include the a u t o m a t i c d e t e c t i o n of core a n d t r t r a d m s m the fingerprmt

REFERENCES

1 L S Penrose. Memorandum on dermatoglyphlc nomenclature. Birth Defects Original Article Series. Vol, 4. No 3. pp 1-13 (1968) 2 J Verbov, Climcal significance and genetics of cpldermal ridges - - a review of dermatoglyphics, J lnve~t Derm 54, 261-271 (1970) 3 S B Holt. The slgmficance of dermatoglyphlcs in medicine, Chn Pediat 12, 471-484 (1973) 4 M Alter, Dermatoglyphlc analysis as a diagnostic tool. Medtcme 16, 35-56 (1966) 5 F Galton, Fmger Prints MacMillan, London (1892) 6 S B Holt and L S Penrose, The genetics of dermal ridges Charles C Thomas, Springfield, Illinois (1968) 7 C R Kingston. Problems in semi-automated fingerprint classification, Law Enforcement Science attd Technology Academic Press, New York (1967) 8 M Traurmg, Automatic comparison of finger-ridge patterns, Nature 938, 938-940 (1963) 9 J H Wegstem and J F Rafferty, Machine oriented fingerprmt classification, Law Enforcement Science and Technology Academic Press, New York (1967) 10 J H Wegstem and J F Rafferty, Matching fingerprints by computer, NBS Techmcal Note 466 (19681 11 C B Shelman. Machine classification of fingerprints. Law Enforcement Science and Technology. pp 467-477 Academic Press. New York (1967) 12 C V K Rao and K Balek. Finding the core point in a fingerprint. IEEE Trans Comput C-27, 77-81 (1978) 13 Cornell Aeronautical Laboratory. I n c . Development and evaluation of a reader of fingerprint minutiae. CAL Report No XM-2478-X-2 (1969) 14 Cornell Aeronautical Laboratory. I n c . Evaluation of an improved reader of bngerprmt minutiae. CAL Report No XM-2478-X-2 (1969) 15 Corncll Aeronautical Laboratory. I n c . Fingerprint reader ~mprovements and regtstrat,on tcchmques. CAL Report No XM-2478-X-3 (197(I) 16 F Pernus. S Kovaclc and L Gyergyek. Mmutme based fingerprint registration. Proc 5th lnt Joint Conf on Pattern Recognmon. Mmml. pp 1380-1382 (1980)

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17 A Grasselh, On the automatic classification of fingerprints - - some considerations of the hnguisttc mterpretatlon of pictures, Methodologies of Pattern Recognition. S Watanabe ed . pp 253-273 Academic Press. New York (1969) 18 G Lew and F Sirovzch, Structural descriptions of fingerprmt amages, Inf Sct 4, 327-355 (1972) 19 W J Hankley and J T Tou. Automatic bngcrprmt interpretation and classification via contextual analys~s and topological coding Pwtonal Pattern Recognition. G C Cheng. R S Lcdlcy. D K Pollock and A Roscnfcld c d s . pp 4 l l - 4 5 6 Thompson Book C o . Washington DC ( 19681 20 J T Tou. On feature encoding in picture processing by computer, Proc 7th Annual Allerton Conf on Circuits and System Theory. Umv of llhnols (1969) 21 R P Chlralo and L L Berdan. Adaptive digital enhancement of latent fingerprints. Proc Carnahan Conf on Crime Countermeasurements. pp 131-135 (19681 22 C V K Rao. B Prasada and K R Sarma. An automatic fingerprint classification system. Proc 2nd Int Joint Conf on Pattern Recognition. pp 180-184 (1974) 23 T Ch M Rao. Feature extraction for fingerprint classification, Pattern Recognmon 8, 181-192 (1976) 24 C V K Rao, On fingerprint pattern recognmon. Pattern Recognition 10, 15-18 (1978) 25 C V K Rao and K Balck, Type classification of fingerprmts a syntactic approach. IEEE Trans Pattern Anal Mach InteU PAMI-2, 223-231 (1980) 26 B Moayer and K S Fu, A syntactic approach to fingerprmt pattern recogmtlon. Pattern Recognmon 7, 1-23 (1975) 27 B Moaycr and K S Fu, An apphcatlon of stochastic languages to fingerprint pattern recogmtlon. Pattern Recognmon 8, 173-179 (1976) 28 B Moayer and K S Fu. A tree system approach for fingerprint pattern recogmtlon. IEEE Trans Comput C-25, 262-274 (19761 29 R C Dubes and C E Rupe. A pattern recogmtton study of dermatoglyphlc traits. Proc San Diego Btomedwal Symposium. Vol 13. 203-213 (19741 311 G S Deutsch. Throning algorithm on rectangular hexagonal and triangular arrays. Communs Ass cornput Mach 15, 827-837 (19721 31 P V C Hough. Methods and means for recognizing complex patterns. U S Patent 3069654 (1962) 32 R O Duda and P E Hart. Use of the Hough transform to detcct lines and curves in p~ctures. Communs A~ ~omput Mach 15, 11-15 (1972) 33 A lannmo and S D Shapiro. A survcy of the Hough transform and ~ts extensions for curve detection. Proc IEEE-C3 Con/ on Pattern Recognition and Image Processing. Chicago. pp 32-38 (1978) 34 C J Hdd~tch. Linear skeletons from square cupboards. Mach Intell 4, 403-420 (1969) 35 M H Hueckel. A local visual edge operator which recognizes edges and hncs. J Ass comput Mach 20, 634-647 (1973)

About the Author--WLt-CtluN~, LJN received the B S degree in electrical engineering from the Na-

tional Talwan Umverslty. Talpel. Tatwan. m 1975. the M B A degree lrom the National Chench~ Umverslty, Tatpel, Talwan, m 1977 and the M S degree In computer science from Michigan State University m 1980 From 1980 to 1981 he was a research assistant in the Pattern Recogmtlon and Image Processmg Laboratory of Michigan State University He is currently enrolled as a Ph D student m the School of Electrical Engineering and is a research assistant in the Advanced Automation Research Laboratory of Purdue University H~s research interests include image processing, pattern recogmt~on, computer graphics and parallel processing He is a member of the IEEE Computer Society. ACM and the Pattern Recognition Society

8

WEI-CHuNG L|N and RICHARD C DUBES About the Author--RICHARD C DUBES was born m Chicago, Ilhnols He received the B S degree

from the Umversity of Illinois, Urbana, m 1956 and the M S and Ph D degrees from Michigan State Umverstty, East Lansing. in 1959 and 1962, respectively, all m electrical engineering In 1956 and 1957 he was a member of the Technical Staff of the Hughes Aircraft Company. Culver City. Cahforma From 1957 through 1968 he served as Graduate Assistant, Research Assistant. Assistant Professor and Associate Professor in the Electrical Engineering Department at Mmhtgan State Umverstty In 1969 he lomed the Computer Soence Department at Michigan State Umversity and became Professor in 1970 His areas of technical interest mclude pattern recognmon, exploratory data analyms. image processing and the apphcatlon of data analysis methods to the medical area Dr Dubes is a member of the Pattern Recogmtlon Society, IEEE, Sigma Xl and the Class,ficatton Sooely