http://www.diva-portal.org This is the published version of a paper presented at 3rd COST-275 Workshop on Biometrics on the Internet, COST-275, Hatfield, United Kingdom, 27-28 October, 2005. Citation for the original published paper : Alonso-Fernandez, F., Fierrez-Aguilar, J., Ortega-Garcia, J. (2005) A Review Of Schemes For Fingerprint Image Quality Computation. In: Aladdin Ariyaeeinia, Mauro Falcone & Andrea Paoloni (ed.), COST Action 275: Proceedings of the third COST 275 Workshop Biometrics on the Internet (pp. 3-6). Luxembourg: EU Publications Office (OPOCE) N.B. When citing this work, cite the original published paper. Permanent link to this version: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-21211
Transcript
http://www.diva-portal.org
This is the published version of a paper presented at 3rd COST-275 Workshop on Biometrics on theInternet, COST-275, Hatfield, United Kingdom, 27-28 October, 2005.
Citation for the original published paper:
Alonso-Fernandez, F., Fierrez-Aguilar, J., Ortega-Garcia, J. (2005)
A Review Of Schemes For Fingerprint Image Quality Computation.
In: Aladdin Ariyaeeinia, Mauro Falcone & Andrea Paoloni (ed.), COST Action 275: Proceedings of
the third COST 275 Workshop Biometrics on the Internet (pp. 3-6). Luxembourg: EU Publications
Office (OPOCE)
N.B. When citing this work, cite the original published paper.
Permanent link to this version:http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-21211
European Co-Operation in the Field of Scientific and Technical Research
Telecommunications, Information Science & Technology
Third COST 275 Workshop
Biometrics on the Internet
University of Hertfordshire, UK 27-28 October 2005
PROCEEDINGS
A REVIEW OF SCHEMES FOR FINGERPRINT IMAGE QUALITY COMPUTATION
Fernando Alonso-Fernandez, Julian Fierrez-Aguilar, Javier Ortega-Garcia
Biometrics Research Lab.- ATVS, Escuela Politecnica Superior - Universidad Autonoma de MadridAvda. Francisco Tomas y Valiente, 11 - Campus de Cantoblanco - 28049 Madrid, Spain
Fingerprint image quality affects heavily the performance offingerprint recognition systems. This paper reviews existingapproaches for fingerprint image quality computation. Wealso implement, test and compare a selection of them usingthe MCYT database including 9000 fingerprint images. Ex-perimental results show that most of the algorithms behavesimilarly.
1. INTRODUCTION
Due to its permanence and uniqueness, fingerprints arewidely used in many personal identification systems. Fin-gerprints are being increasingly used not only in forensicenvironments, but also in a large number of civilian appli-cations such as access control or on-line identification [1].
The performance of a fingerprint recognition system isaffected heavily by fingerprint image quality. Several fac-tors determine the quality of a fingerprint image: skin con-ditions (e.g. dryness, wetness, dirtiness, temporary or per-manent cuts and bruises), sensor conditions (e.g. dirtiness,noise, size), user cooperation, etc. Some of these factorscannot be avoided and some of them vary along time. Poorquality images result in spurious and missed features, thusdegrading the performance of the overall system. Therefore,it is very important for a fingerprint recognition system toestimate the quality and validity of the captured fingerprintimages. We can either reject the degraded images or adjustsome of the steps of the recognition system based on theestimated quality.
Fingerprint quality is usually defined as a measure ofthe clarity of ridges and valleys and the “extractability” ofthe features used for identification such as minutiae, coreand delta points, etc [2]. In good quality images, ridges andvalleys flow smoothly in a locally constant direction [3].
In this work, we review the algorithms proposed forcomputing fingerprint image quality. We also implement,test and compare a selection of them using the MCYTdatabase [4, 5].
The rest of the paper is organized as follows. We re-view existing algorithms for fingerprint image quality com-
Fingerprint Image Quality Computation Methods
Based on Local Features
•Orientation Field
•Gabor filter responses
•Pixel intensity
Based on Global Features
•Orientation Field
•Power spectrum
Based on Classifiers
•Neural Networks
Fingerprint Image Quality Computation Methods
Based on Local Features
•Orientation Field
•Gabor filter responses
•Pixel intensity
Based on Local Features
•Orientation Field
•Gabor filter responses
•Pixel intensity
Based on Global Features
•Orientation Field
•Power spectrum
Based on Global Features
•Orientation Field
•Power spectrum
•Orientation Field
•Power spectrum
Based on Classifiers
•Neural Networks
Based on Classifiers
•Neural Networks
Fig. 1. A taxonomy of fingerprint image quality computa-tion algorithms.
putation in Sect. 2. An experimental comparison betweenselected techniques is reported in Sect. 3. Conclusions arefinally drawn in Sect. 4.
2. FINGERPRINT IMAGE QUALITYCOMPUTATION
A taxonomy of existing approaches for fingerprint imagequality computation is shown in Fig. 1. We can divide theexisting approaches intoi) those that use local features ofthe image;ii) those that use global features of the image;and iii) those that address the problem of quality assess-ment as a classification problem.
2.1. Based on local features
Methods that rely on local features[2, 3, 6-8] usually dividethe image into non-overlapped square blocks and extractfeatures from each block. Blocks are then classified intogroups of different quality. Alocal measure of qualityisfinally generated. This local measure can be the percent-age of blocks classified with “good” or “bad” quality, oran elaborated combination. Some methods assign a relativeweight to each block based on its distance from the centroidof the fingerprint image, since blocks near the centroid aresupposed to provide more reliable information [2, 8].
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2.1.1. Based on the orientation field
This group of methods use the local angle information pro-vided by the orientation field to compute several local fea-tures in each block. Hong et al. [3] modeled ridges and val-leys as a sinusoidal-shaped wave along the direction normalto the local ridge orientation and extracted the amplitude,frequency and variance of the sinusoid. Based on these pa-rameters, they classify the blocks asrecoverableandunre-coverable. If the percentage of unrecoverable blocks ex-ceeds a predefined threshold, the image is rejected. Themethod presented by Lim et al. [6] computes the followingfeatures in each block: orientation certainty level, ridge fre-quency, ridge thickness and ridge-to-valley thickness ratio.Blocks are then labeled as “good”, “undetermined”, “bad”or “blank” by thresholding the four local features. A localquality scoreSL is computed based on the total number of“good”, “undetermined” and “bad” quality image blocks.Recently, Chen et al. [2] proposed a local quality indexwhich measures the spatial coherence using the intensitygradient. The orientation coherence in each block is com-puted. A local quality scoreQS is finally computed by aver-aging the coherence of each block, weighted by its distanceto the centroid of the foreground.
2.1.2. Based on Gabor filters
Shen et al. [7] proposed a method based on Gabor features.Each block is filtered using a Gabor filter withm differentorientations. If a block has good quality (i.e. strong ridgeorientation), one or several filter responses are larger thanthe others. In poor quality blocks or background blocks,them filter responses are similar. The standard deviation ofthem filter responses is then used to determine the qualityof each block (“good” and “poor”). A quality indexQI ofthe whole image is finally computed as the percentage offoreground blocks marked as “good”. IfQI is lower thana predefined threshold, the image is rejected. Poor qualityimages are additionally categorized as “smudged” or “dry”.
2.1.3. Based on pixel intensity
The method described in [8] classifies blocks into “direc-tional” and “non-directional” as follows. The sum of inten-sity differencesDd (i, j) between a pixel(i, j) and l pix-els selected along a line segment of orientationd centeredaround(i, j) is computed forn different orientations. Foreach different orientationd, the histogram ofDd (i, j) val-ues is obtained for all pixels within a given foreground block.If only one of then histograms has a maximum value greaterthan a prominent threshold, the block is marked as “direc-tional”. Otherwise, the block is marked as “non-directional”.
An overall quality scoreQ is finally computed. A rel-ative weightwi is assigned to each foreground block based
on its distance to the centroid of the foreground.Q is de-fined asQ =
∑D wi /
∑F wi whereD is the set of direc-
tional blocks andF is the set of foreground blocks. IfQis lower than a threshold, the image is considered to be ofpoor quality. Measures of the smudginess and dryness ofpoor quality images are also defined.
2.2. Based on global features
Methods that rely on global features [2, 6] analyze the over-all image and compute aglobal measure of qualitybased onthe features extracted.
2.2.1. Based on the orientation field
Lim et al. [6] presented two features to analyze the globalstructure of a fingerprint image. Both of them use the localangle information provided by the orientation field, whichis estimated in non-overlapping blocks. The first featurechecks the continuity of the orientation field. Abrupt orien-tation changes between blocks are accumulated and mappedinto a global orientation scoreSGO. The second featurechecks the uniformity of the frequency field [9]. This isdone by computing the standard deviation of the ridge-to-valley thickness ratio and mapping it into a global scoreSGR. Although ridge-to-valley thickness is not constantin fingerprint images in general, the separation of ridgesand valleys in good quality images is more uniform than inlow quality ones. Thus, large deviation indicates low imagequality.
2.2.2. Based on Power Spectrum
Global structure is analyzed in [2] by computing the 2D Dis-crete Fourier Transform (DFT). For a fingerprint image, theridge frequency value lies within a certain range. A regionof interest (ROI) of the spectrum is defined as an annularregion with radius ranging between the minimum and max-imum typical ridge frequency values. As fingerprint imagequality increases, the energy will be more concentrated inring patterns within the ROI. The global quality indexQF
defined in [2] is a measure of the energy concentration inring-shaped regions of the ROI. For this purpose, a set ofbandpass filters is constructed and the amount of energy inring-shaped bands is computed. Good quality images willhave the energy concentrated in few bands.
2.3. Based on classifiers
The method that uses classifiers [10] defines the quality mea-sure as a degree of separation between the match and non-match distributions of a given fingerprint. This can be seenas a prediction of the matcher performance.
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2.3.1. Based on neural networks
Tabassi et al. [10] presented a novel strategy for estimatingfingerprint image quality. They first extract the fingerprintfeatures used for identification and then compute the qualityof each extracted feature to estimate the quality of the fin-gerprint image, which is defined as the degree of separationbetween the match and non-match distributions of a givenfingerprint.
Let sm (xi) be the similarity score of a genuine compar-ison (match) corresponding to the subjecti, andsn (xji),i 6= j be the similarity score of an impostor comparison(non-match) between subjecti and impostorj. QualityQN
of a biometric samplexi is then defined as the prediction of
o (xi) =sm (xi)− E [sn (xji)]
σ (sn (xji))(1)
whereE[.] is mathematical expectation andσ(.) is standarddeviation. Eq. 1 is a measure of separation between thematchand thenon-matchdistributions, which is supposedto be higher as image quality increases.
The prediction ofo (xi) is done in two steps:i) vi =L (xi); andii) QN = o (xi) = I (vi); whereL(.) computesa feature vectorvi of xi andI(.) maps the feature vectorvi
to a predictiono (xi) of o(xi) by using a neural network.Feature vectorvi contains the following parameters:a)
number of foreground blocks;b) number of minutiae foundin the fingerprint;c) number of minutiae that have qualityvalue higher than 0.5, 0.6, 0.75, 0.8 and 0.9, respectively;andd) percentage of foreground blocks with quality equalto 1, 2, 3 and 4, respectively. All those values are providedby the MINDTCT package of NIST Fingerprint Image Soft-ware (NFIS) [11]. This method uses both local and globalfeatures to estimate the quality of a fingerprint.
3. EXPERIMENTS
In this work, we have implemented an tested some of thealgorithms presented above using the existing fingerprintimage database MCYT [4, 5]. In particular, 9000 finger-print images from all the fingers of 75 subjects are consid-ered (QMCYT subcorpus from now on). Fingerprints areacquired with an optical sensor, model UareU from DigitalPersona, with a resolution of 500 dpi and a size of 400 pixelsheight and 256 pixels width. A subjective quality assess-mentQM of this database was accomplished by a humanexpert. Each different fingerprint image has been assigneda subjective quality measure from0 (lowest quality) to9(highest quality) based on factors like: captured area of thefingerprint, pressure, humidity, amount of dirt, and so on.
The algorithms tested in this work are as follows:i)the combined quality measureQC computed in [6] by lin-early combining the scoresSL, SGO andSGR presented in
Fig. 2. Sample images extracted from the five quality sub-sets created using the manual quality measureQM . Imagesare arranged by increasing quality (on the left: lowest qual-ity, subset 1; on the right: highest quality, subset 5).
Sects. 2.1.1 and 2.2.1;ii) the algorithms presented in [2]based on localQS (Sect. 2.1.1) and global featuresQF
(Sect. 2.2.2); andiii) the methodQN based on neural net-works proposed in [10] and described in Sect. 2.3.1. Thequality measuresQC , QS and QF lie in the range[0, 1]whereasQN ∈ {1, 2, 3, 4, 5}. The selected methods arealso compared with the subjective quality assessmentQM
accomplished in QMCYT.The above-mentioned quality measures have been com-
puted for all QMCYT. In order to compare the selected meth-ods, we have arranged the fingerprint images by increasingquality measureQk, k ∈ {M, N,C, S, F}. Then, 5 subsetsSi
k, i ∈ {1, 2, 3, 4, 5}, of equal size (1800 images per sub-set) are created. The first subset contains the 1800 imageswith the lowest quality measures, the second subset containsthe next 1800 images with the lowest quality measures, andso on. Sample images extracted from the five quality subsetscreated using the manual quality measureQM are shownin Fig. 2. The mean quality measure of each subsetSi
k
is then computed asQik = 1
1800
∑j∈Si
kQi
k(j)n(j) wheren(j) is the total number of images with quality measureQi
k(j). Lastly, mean quality measuresQik are normalized to
the[0, 1] range as follows:Qik =
(Qi
k − Q1k
)/(Q5
k − Q1k
)
whereQik is the normalized mean quality measure ofQi
k.In Fig. 3, we can see the normalized mean quality mea-
suresQik of each subsetSi
k, i ∈ {1, 2, 3, 4, 5}, for all thekalgorithms tested,k ∈ {M, N, C, S, F}. Maximum value,minimum value and standard deviation value of normalizedindividual quality measures of each subset are also depicted.It can be observed that that most of the algorithms result insimilar behavior, assigning well-separated quality measuresto different quality groups. Only the algorithm based onclassifiers,QN , results in very different behavior, assigningthe highest quality value to more than half of the database.It may be due to the low number of quality labels used bythis algorithm [10].
Regarding to the algorithms that behave similarly, it canbe observed that standard deviation is similar for qualitygroups 2 to 4. Only the method based on the subjective qual-ity assessmentQM results in slightly higher deviation. This
Fig. 3. Normalized mean quality measureQik of quality groupi ∈ {1, 2, 3, 4, 5}, for all thek algorithms tested (M=Manual,
C=Combined local+global features [6], S=local spatial features [2], F=global frequency [2], N=classifier based on neuralnetworks [10]). Maximum value, minimum value and standard deviation value of normalized quality measures of eachquality group are also depicted.
is maybe due to the finite number of quality labels used. Theother algorithms assign continuous quality measures withina certain range.
In addition, in most of the quality groups, normalizedquality measures lie within a range of 2 times the standarddeviation. Only quality groups 1 and 5 sometimes behavedifferent, maybe to the presence of outliers (i.e., imageswith very low quality measure in group 1 and with very highquality measure in group 5, respectively).
4. CONCLUSIONS AND FUTURE RESEARCH
This paper reviews most of the existing algorithms proposedto compute the quality of a fingerprint image. They canbe divided intoi) those that use local features of the im-age;ii) those that use global features of the image; andiii)those that address the problem of quality assessment as aclassification problem. We have implemented and tested aselection of them. They are compared with the subjectivequality assessment accomplished in the existing QMCYTsubcorpus. Experimental results show that most of the al-gorithms behave similarly, assigning well-separated qualitymeasures to different quality groups. Only the algorithmbased on classifiers [10] results in very different behavior.It may be due to the low number of quality labels used bythis algorithm. Future work includes integrating the imple-mented quality estimation algorithms into a quality-basedmultimodal authentication system [12].
Acknowledgments
This work has been supported by BioSecure European NoEand the TIC2003-08382-C05-01 project of the Spanish Min-istry of Science and Technology. F. A.-F. and J. F.-A. thankConsejeria de Educacion de la Comunidad de Madrid andFondo Social Europeo for supporting their PhD studies.
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