Fingerprint image enhancement: algorithm and performance evaluation
ABSTRACT In order to ensure that the performance of an automatic
fingerprint identification/verification system will be robust with
respect to the quality of input fingerprint images, it is essential to
incorporate a fingerprint enhancement algorithm in the minutiae
extraction module. We present a fast fingerprint enhancement algorithm,
which can adaptively improve the clarity of ridge and valley structures
of input fingerprint images based on the estimated local ridge
orientation and frequency. We have evaluated the performance of the
image enhancement algorithm using the goodness index of the extracted
minutiae and the accuracy of an online fingerprint verification system.
Experimental results show that incorporating the enhancement algorithm
improves both the goodness index and the verification accuracy
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Conference Proceeding: On-line fingerprint verification[show abstract] [hide abstract]
ABSTRACT: We describe the design and implementation of an online fingerprint verification system which operates in two stages: (i) minutia extraction and (ii) minutia matching. An improved minutia extraction algorithm that is much faster and more accurate than our earlier algorithm has been implemented. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to adaptively compensate for the nonlinear deformations and inexact pose transformations between finger prints. The system has been tested on two sets of finger print images captured with inkless scanners. The verification accuracy is found to be over 99% with a 15% reject rate. Typically, a complete fingerprint verification procedure takes, on an average, about 8 seconds on a SPARC 20 workstation. It meets the response time requirements of on-line verification with high accuracyPattern Recognition, 1996., Proceedings of the 13th International Conference on; 09/1996
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ABSTRACT: Two-dimensional spatial linear filters are constrained by general uncertainty relations that limit their attainable information resolution for orientation, spatial frequency, and two-dimensional (2D) spatial position. The theoretical lower limit for the joint entropy, or uncertainty, of these variables is achieved by an optimal 2D filter family whose spatial weighting functions are generated by exponentiated bivariate second-order polynomials with complex coefficients, the elliptic generalization of the one-dimensional elementary functions proposed in Gabor's famous theory of communication [J. Inst. Electr. Eng. 93, 429 (1946)]. The set includes filters with various orientation bandwidths, spatial-frequency bandwidths, and spatial dimensions, favoring the extraction of various kinds of information from an image. Each such filter occupies an irreducible quantal volume (corresponding to an independent datum) in a four-dimensional information hyperspace whose axes are interpretable as 2D visual space, orientation, and spatial frequency, and thus such a filter set could subserve an optimally efficient sampling of these variables. Evidence is presented that the 2D receptive-field profiles of simple cells in mammalian visual cortex are well described by members of this optimal 2D filter family, and thus such visual neurons could be said to optimize the general uncertainty relations for joint 2D-spatial-2D-spectral information resolution. The variety of their receptive-field dimensions and orientation and spatial-frequency bandwidths, and the correlations among these, reveal several underlying constraints, particularly in width/length aspect ratio and principal axis organization, suggesting a polar division of labor in occupying the quantal volumes of information hyperspace.(ABSTRACT TRUNCATED AT 250 WORDS)Journal of the Optical Society of America. A, Optics and image science 08/1985; 2(7):1160-9.
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ABSTRACT: Fingerprint filter design and a method of enhancing fingerprint images are discussed. Two distinct filters in the Fourier domain are designed, a frequency filter corresponding to ridge frequencies and a direction filter corresponding to ridge directions on the basis of fingerprint ridge characteristics. An energy function for selecting image features (i.e., frequencies and directions) is defined by power of images obtained with the above filters and a measure of smoothness of the features. Using the image features that minimize the energy function, we produce an enhanced image from the filtered images. This image enhancement method is applied to fingerprint matching. In experiments with rolled prints, it is shown that the false identification rate is reduced by about two thirds compared with Asai’s method.05/2007: pages 113-126;
P erformance Ev aluation
Lin Hong?YifeiWan? andAnilJain
Michigan StateUniv ersity
A criticalstep inautomatic?ngerprint matchingistoautomatical ly andreliablyextract
minutiaefromtheinput?ngerprintimages?However?the performanceof aminutiaeex?
andfurrowstructures of input?ngerprint imagesbasedontheestimate dlo cal ridgeorienta?
usingthegoodnessindexoftheextr actedminutiae andtheaccuracyofanonline?ngerprint
Fingerprintidenti?cationis oneofthemostimportantbiometrictec hnologieswhichhas
drawnasubstantialamountofattentionrecently ????????A?ngerprintisthepattern of
ridges andfurrowsonthe surfaceofa?ngertip?Eachindividualhasunique?ngerprints?
Theuniqueness ofa?ngerprintisexclusiv elydeterminedbythelocal ridgecharacteristics
Ridge Bifurcation Ridge Ending
Figure??Examplesof minutiae??a?aminutiaecanbecharacterizedbyitsp ositionand its
orientation??b?minutiaeov erlaidon a?ngerprint image?
andtheir relationships????????Atotalofonehundredand?fty di?erentlocalridgechar?
qualityof ?ngerprints andarerarelyobservedin?ngerprints?Thetwomostprominentridge
characteristics? calledminutiae? are?i?ridge endingand?ii? ridge bifurc ation?Aridge end?
ingisde?nedasthepointwherearidge endsabruptly?Aridgebifurcationis de?nedasthe
containsabout??????minutiae? Examplesofminutiaearesho wnin Figure??
Automatic ?ngerprint matc hingdep endsonthecomparisonoftheselocalridgec har?
acteristics andtheirrelationshipstomakeapersonal iden ti?cation?????Acriticalstepin
print images?whichisadi?culttask?Theperformanceofaminutiaeextraction algorithm
relies heavilyon the quality of theinput?ngerprintimages? Inanideal?ngerprintimage?
ridgesandfurro wsalternateand?ow ina locally constan tdirection andmin utiae areanoma?
Figure ??Fingerprintimages ofverypoorqualit y?
liesofridges?i?e?ridgeendingsandridgebifurcations?Insuch situations?the ridges canbe
easilydetectedandminutiaecanbe preciselylocatedfrom thebinary ridges? Figure??b?
sho wsanexampleof good quality liv e?scan?ngerprint image?Howev er?in practice?due
tovariationsin impressionconditions?ridgecon?guration?skin conditions ?aberrant forma?
?ngerprintimages?appro ximately???accordingtoourexperience? isofpoorquality?The
cannotbe correctlydetected?Thisleadstofollowingproblems? ?i?asigni?cantnumber of
spuriousminutiaemaybecreated??ii?alargepercentofgenuine min utiaemaybeignored?
and?iii?largeerrors intheirlocalization ?p osition andorien tation?maybein troduced?Ex?
amplesof?ngerprintimages ofverypo orquality? inwhic h ridge structuresare completely
corrupted?are sho wn inFigure ?? Inordertoensurethatthep erformance of themin utiae
Figure??Fingerprintregions??a?well?de?ned region??b?recoverable corrupted region??c?
unreco verable corrupted region?
extractionalgorithmwillbe robustwithresp ect tothequalit yofinputdigital?ngerprint
images?anenhancemen talgorithmwhichcanimprovethe clarityoftheridgestructuresis
visualcluessuchas localridgeorientation?ridgecontinuity?ridge tendency? etc?? aslong
astheridge andfurrowstructuresare notcorruptedcompletely?Itis possibleto develop
anenhancementalgorithmthatexploits thesevisual cluestoimprovetheclarit y ofridge
structures incorrupted?ngerprintimages?Generally? foragiv endigital?ngerprintimage?
theregion ofinterest canbedivided intothefollowingthreecategories?Figure???
?Well?de?nedregion? whereridgesandfurrows areclearlydi?erentiatedfrom onean?
other suchthataminutiaeextractionalgorithmis abletooperatereasonably?
?Recover ablecorruptedregion? whereridgesand furrowsarecorruptedbya smallamount
ofcreases? smudges?etc?But?theyarestillvisibleandtheneighboringregionspro vide
su?cientinformation aboutthe trueridgeandfurrowstructures?
?Unrecoverablec orruptedregion?where ridgesandfurrows arecorruptedbysucha
sev ereamount ofnoise anddistortion thatno ridgesandfurrowsarevisibleandthe
neighboringregionsdonotpro videsu?cientinformationaboutthe trueridgeand
furrow structures either?
Werefertothe?rsttwocategoriesofregions asrecoverable and thelast categoryasunrecov?
erable?Thegoal ofan enhancement algorithmistoimpr ovetheclarityofridgestructur esof
?ngerprintimages inreco verableregionsand toremo vetheunrecoverableregions?Sincethe
ofinput ?ngerprintimagestofacilitatetheextractionofridgesandminutiae?a?ngerprin t
enhancementalgorithm should notresultin anyspurious ridgestructures?Thisisveryim?
portantb ecausespuriousridgestructure maychange theindividualityofinput?ngerprints?
Fingerprin t enhancementcanbeconducted on either?i?binaryridgeimagesor?ii?gray?
levelimages?Abinaryridgeimageisanimagewhereall the ridgepixelsareassignedav alue
?and non?ridgepixelsare assignedavalue ??Thebinary imagecanbeobtained by applying
aridgeextractionalgorithmonagra y?level?ngerprint image???? Sinceridges andfurrows
ina?ngerprintimagealternate andrunparallelto eachotherinalo calneighborho od?a
numberofsimpleheuristics canbeused todi?erentiate thespuriousridgecon?gurations
from thetrue ridgecon?gurationsina binaryridge image???? Howev er? afterapplying
aridgeextractionalgorithmon the originalgra y?levelimages?informationaboutthetrue
ridgestructuresisoftenlost dependingonthe performance oftheridgeextractionalgorithm?
Therefore?enhancementofbinary ridgeimageshasits inherent limitations?
Ina gray?level ?ngerprintimage?ridgesandfurrows ina local neighb orhood forma