FingerCode: A Filterbank for Fingerprint Representation and
`Matching*
`
`Ani1 K. Jain, Salil Prabhakar, Lin Hong,
`Department of Computer Science and Engineering
`Michigan State University, East Lansing, MI 48824
`{ jain,prabhaka,honglin} @cse.msu .edu
`
`and Sharath Pankanti
`IBM T. J. Watson Research Center
`Yorktown Heights, NY 10598
`sharat @watson.ibm .com
`
`Abstract
`With the identity fraud in o u r society reaching u n -
`precedented proportions and with a n increasing empha-
`sis o n the emerging automatic positive personal iden-
`tification applications, biometrics-based identification,
`especially fingerprint-based identification, is receiving
`a lot of attention. There are t w o m a j o r shortcomings
`of the traditional approaches t o fingerprint represen-
`tation. For a significant fraction of population, the
`. representations based o n explicit detection of complete
`ridge structures in the fingerprint are dificult t o ex-
`tract automatically. T h e widely used minutiae-based
`representation does n o t utilize a significant component
`of the..riCh discriminatory information available in the
`fingerprints. T h e proposed filter-based algorithm uses
`a bank of Gabor filters t o capture both the local and
`the global details in a fingerprint as a compact 640-
`byte fixed length FingerCode. T h e fingerprint match-
`ing is based o n the Euclidean distance between the
`t w o corresponding Fingercodes and hence is extremely
`fast. Our initial results show identification accuracies
`comparable t o the best results of minutiae-based algo-
`rithms published in the open literature [.I. Finally, w e
`show that the matching performance can be improved
`by combining the decisions of the matchers based o n
`complementary fingerprint information.
`1 Introduction
`With the advent of electronic banking, e-commerce,
`and smartcards and an increased emphasis on the
`privacy and security of information stored in various
`databases, automatic personal identification has be-
`come a very important topic. Accurate automatic per-
`sonal identification is now needed in a wide range of
`civilian applications such as passport control, cellu-
`lar telephones, automatic teller machines, and driver
`licenses. Traditional knowledge-based (password or
`Personal Identification Number (PIN)) and token-
`
`'This work is partially supported by IBM Contract No.
`11 1001069.
`
`Figure 1: Minutiae features: a ridge ending (U) and a
`bifurcation (0).
`
`based (passport, drivers license, and ID card) identi-
`fications are prone to fraud because PINS may be for-
`gotten or guessed by an imposter and the tokens may
`be lost or stolen. As an example, credit card fraud
`alone now costs more than 6 billion dollars annually.
`Biometrics, which refers to identifying an individual
`based on his or her physiological or behavioral charac-
`teristics is more reliable in differentiating between an
`authorized person and an imposter.
`Among all the various biometrics (e.g., face, fin-
`gerprints, iris, etc.) , fingerprint-based identification
`is the most mature and proven technique. A finger-
`print is the pattern of ridges and furrows on the sur-
`face of the finger. The uniqueness of a fingerprint
`can be determined by the overall pattern of ridges
`and furrows as well as the local ridge anomalies (a
`ridge bifurcation or a ridge ending, called minutiae
`points (see Figure 1)). As fingerprint sensors are be-
`coming smaller and cheaper [l], automatic identifica-
`tion based on fingerprint is becoming an attractive
`alternative/complement to the traditional methods of
`identification. The critical factor for the widespread
`use of fingerprints is in meeting the performance (e.g.,
`matching speed and accuracy) standards demanded by
`
`0-7695-0 149-4199 $10.00 0 1999 IEEE
`
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`Input Image
`
`Normalize each sector
`
`Filtering
`
`Compute S.D.
`
`I
`
`I\
`
`e .
`
`e .
`
`Template FingerCode
`
`Input FingerCode
`
`U
`I
`
`Calculate Euclidian distance
`
`t
`Matching result
`
`Figure 2: Flow diagram of our fingerprint identification system.
`
`emerging civilian identification applications. Some of
`these applications (e.g., fingerprint-based smartcards)
`will also benefit from a compact representation of a
`fingerprint.
`
`The popular fingerprint representation schemes
`have evolved from intuitive system design tailored for
`fingerprint experts who visually match fingerprints.
`These schemes are either predominantly local (e.g.,
`minutiae-based fingerprint matching systems [2, 31)
`or exclusively global (fingerprint classification based
`on Henry system [4, 51). The minutiae-based auto-
`matic identification techniques first locate the minu-
`tiae points and then match their relative placements
`in a given finger and the stored template [2]. A good
`quality fingerprint contains between 60 to 80 minu-
`tiae, but different fingerprints have different number
`
`of minutiae. The variable sized minutiae-based repre-
`sentation does not easily lend itself to indexing mech-
`anisms; typical approaches [2] to match the minutiae
`from two fingerprints need to align the unregistered
`minutiae patterns of different sizes which makes them
`computationally expensive. The variable length of fin-
`gerprint representation (in terms of position and ori-
`entation of minutiae) makes it unsuitable to store the
`fingerprint on a smartcard. The global approach to
`fingerprint representation is typically used for index-
`ing, but does not offer good individual discrimination.
`Further, the indexing efficacy of existing global repre-
`sentations is poor due to a small number of categories
`that can be effectively identified and a highly skewed
`distribution of the population in each category. Both
`these approaches utilize representations which cannot
`
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`be easily extracted from poor quality fingerprints.
`It is desirable to explore representation schemes
`which combine global and local information in a fin-
`gerprint. We present a new representation for the fin-
`gerprints which yields a relatively short, fixed length
`code, called Fingercode suitable for matching as well
`as storage on a smartcard. The matching reduces to
`finding the Euclidean distance between these Finger-
`Codes and hence the matching is very fast and the
`representation is amenable to indexing. We make use
`of both the global flow of ridges and furrows structure
`and the local ridge characteristics to generate a short
`fixed length code for the fingerprints while maintain-
`ing a high recognition accuracy.
`The proposed scheme of feature extraction tessel-
`lates the region of interest of the given fingerprint im-
`age with respect to a frame of reference (Figure 2).
`A feature vector is composed of an ordered enumer-
`ation of the features extracted from the (local) in-
`formation from each subimage (sector) specified by
`the tessellation. Thus, the feature elements capture
`the local information and the ordered enumeration of
`the tessellation captures the invariant global relation-
`ships among the local patterns. The local discrimi-
`natory information in the sector needs to be decom-
`posed into separate components. Gabor filterbank is a
`well known technique to capture useful information in
`specific bandpass channels as well as decompose this
`information into orthonormal components in terms of
`spatial frequencies.
`2 Filter-based Feature Extraction
`The three main steps in our feature extraction al-
`gorithm are: (i) determine a reference frame for the
`fingerprint image, (ii) filter the image in eight differ-
`ent directions using a bank of Gabor filters, and (iii)
`compute the standard deviation of gray values in sec-
`tors around the reference point in filtered images to
`define the feature vector or the Fingercode.
`
`Figure 3: Reference point (x), the region of interest
`and 80 sectors superimposed on a fingerprint.
`
`Let I(x,y) denote the gray level at pixel (x,y) in
`an M x N fingerprint image and let (xc,gc) denote
`the reference point. The region of interest is defined
`as the collection of all the sectors Si, where the ith
`sector S; is computed in terms of parameters ( T , 0) as
`follows:
`Si = {(x,y) Ib(Ti + 1) I r < b(T; + 2),
`0i 5 0 < o i + l , 1 I x 5 N , 1 F Y 5 M ) ,(I)
`where Ti = i diu k , 0i = (i mod k)(27r/k), T =
`+ (Y - Yc)2, 0 = t a n - l ( ( y -Yc)/(. - x c ) ) ,
`& -
`b is the width of each band and k is the number of
`sectors considered in each band. We consider five con-
`centric bands around the detected reference point for
`feature extraction. Each band is 20-pixels wide ( b =
`20), and segmented into sixteen sectors (k = 16) (Fig-
`ure 3). A 20-pixel wide band captures about 2 ridges
`and furrows on an average, in a 500 dpi fingerprint
`image. The innermost band is not used for feature
`extraction because the sectors in the region near the
`reference point contain very few pixels and, therefore,
`the standard deviation estimates in this region are not
`very reliable. Thus, we have a total of 16 x 5 = 80 sec-
`tors (So through S79). Eighty features for each of the
`eight filtered images give us a total of 640 (80 x 8)
`features per fingerprint image. Each feature can be
`quantized into 256 values and requires 1 byte of stor-
`age, so the entire feature vector requires only 640 bytes
`of storage.
`It is difficult to rely on feature extraction based
`on explicit detection of structural features in (e.g.,
`poor quality) fingerprints; features based on statisti-
`cal properties of images are likely to degrade gracefully
`with the image quality deterioration. For this study,
`we rely on grayscale variance-based (e.g., standard de-
`viation) features. The grayscale standard deviation in
`an image sector is indicative of the overall ridge activ-
`ity. As noted in the Section 3, our matcher based on
`this simple statistical feature performs extremely well
`and we expect to achieve significantly better accura-
`cies with more discriminative attributes.
`It is desirable to obtain representations for finger-
`prints which are scale, translation, and rotation in-
`variant. Scale invariance is not a significant problem
`since most fingerprint images could be scaled as per
`the spatial resolution (dpi) of the sensor. The rota-
`tion and translation invariance could be accomplished
`by establishing a reference frame based on the intrin-
`sic fingerprint characteristics which are rotation and
`translation invariant.
`2.1 Reference Frame Determination
`Fingerprints have many conspicuous landmark
`structures and any combination of them could be used
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`for establishing a reference frame determination. We
`define the reference point of a fingerprint as the point
`of maximum curvature of ridges in the fingerprint im-
`age and the reference axis is defined to be the axis
`of local symmetry at the reference point. Reference
`point and reference axis together establish an invari-
`ant frame of reference for a given fingerprint. Refer-
`ence frame detection relies on Poincark index analysis
`and local symmetry detection, similar to the method
`used in [5].
`
`Figure 4: Determining the reference frame.
`
`Our representation scheme tolerates imprecision in
`the estimates of reference frame. Since the fingerprints
`are smoothly flowing ridge patterns, the characteris-
`tics of local neighborhoods change gradually. A small
`perturbation (within one inter-ridge distance unit) is
`likely to change the representation only slightly, so
`the overall variations in the representation of a fin-
`ger due to inaccurate localization is expected to re-
`main small. The detected reference point could be
`as much as 12 pixels (approximately 1 inter-ridge dis-
`tance units) away from its "true" location. Symmetry
`axis detection is very difficult in fingerprint images
`because the upper portion of the fingerprint has cir-
`cular ridges. We do not reply on the symmetry axis t o
`align the fingerprints. We achieve rotation invariance
`by rotating the Fingercode itself during the matching
`stage. Typical outputs of the reference frame deter-
`mination algorithm are shown in Figure 4.
`
`2.2 Filtering
`To remove noise and enhance the ridge and fur-
`row structures, we filter the fingerprint image in dif-
`ferent directions using a bank of Gabor filters [6]. Fin-
`gerprints have local parallel ridges and furrows, and
`well-defined local frequency and orientation. Properly
`tuned Gabor filters can remove noise, preserve the true
`ridge and furrow structures, and provide information
`contained in a particular direction in the image. A
`minutia point is an anomaly in locally parallel ridges
`and this information is captured by the Gabor filters.
`An even symmetric Gabor filter has the following gen-
`
`Figure 5: One of the eight Gabor filters (size = 33 x 33,
`f = 0.1, 6, = 4.0, 6, = 4.0, 8= 0')
`in the spatial
`domain.
`
`era1 form in the spatial domain:
`
`G(x,y;f,O) = I ; ( x 1 , 3 1 1 , 6 x , S y ) C O S ( 2 ~ f x 1 ) , (2)
`where (j'(x', yl, S,, 6,) = ,-0.5((X'*/6~)+(Y'*/6~)),
`2' =
`xsan8 + ycose, y' = xcos8 - ysin8, f is the frequency
`of the sinusoidal plane wave along the direction 8 from
`the x-axis, and 6, and 6, are the space constants of the
`Gaussian envelope along x and y axes, respectively.
`In our experiments, we set the filter frequency f
`to the average ridge frequency (l/K), where K is the
`inter-ridge distance. The average inter-ridge distance
`is approximately 10 pixels in a 500 dpi fingerprint im-
`age. If f is too large, spurious ridges are created in
`the filtered image whereas if f is too small, nearby
`ridges are merged into one. We used eight different
`values for 8 (0", 22.5', 45', 67.5", go", 112.5', 135",
`and 157.5') with respect to the x-axis. One of these
`eight filters is shown in Figure 5. The region of in-
`terest in a fingerprint image is convolved with each
`of these eight filters to produce a set of eight filtered
`images. A fingerprint convolved with a 0'-oriented
`filter accentuates those ridges which are parallel to
`the x-axis and smoothes the ridges in the other di-
`rections. Filters tuned to other directions work in a
`similar way. These eight direction-sensitive filters cap-
`ture most of the global ridge directionality information
`as well as the local ridge characteristics present in a
`fingerprint. We illustrate this by reconstructing a fin-
`gerprint image by adding together all the eight filtered
`images. The reconstructed image is similar to the orig-
`inal image but has been enhanced (Figure 6(h)). At
`least four directions are required to capture the entire
`global ridge information in a fingerprint (Figure 6(g)),
`but eight directions are required to capture the local
`characteristics. By capturing both the global and lo-
`cal information, the verification accuracy is improved
`although there is some redundancy among the eight fil-
`
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`tered images. If the 6, and 6, (standard deviations of
`the Gaussian envelope) values are too large, the filter
`is more robust to noise, but is more likely to smooth
`the image to the extent that the ridge and furrow de-
`tails in the fingerprint are lost. If the 6, and 6, values
`are too small, the filter is not effective in removing
`noise. The values for 6, and 6, were empirically de-
`termined and each is set to 4.0 (about half the average
`inter-ridge distance).
`Before filtering the fingerprint image, we normal-
`ize the region of interest in each sector separately to
`a constant mean and variance. Normalization is per-
`formed to remove the effects of sensor noise and finger
`pressure differences. Let I ( z , y) denote the gray value
`at pixel (x,y),
`variance of sector S,, respectively, and N i ( z , y), the
`normalized gray-level value at pixel (z, y). For all the
`pixels in sector S,, the normalized image is defined as:
`
`and x, the estimated mean and
`
`if I ( Z , Y ) > M
`
`otherwise ,
`
`where MO and VO are the desired mean and variance
`values, respectively. Normalization is a pixel-wise op-
`eration which does not change the clarity of the ridge
`and furrow structures. If normalization is performed
`on the entire image, then it can not compensate for
`the intensity variations in different parts of the fin-
`ger due to finger pressure differences. Normalization
`of each sector separately alleviates this problem. For
`our experiments, we set both MO and Vo to a value of
`100.
`2.3 Feature Vector
`Let F,o(z, y) be the &direction filtered image
`for sector Si. For V i = 0,1,. . .,79 and 8 E
`[O", 22.5",45", 67.5", go", 112.5", 135", 157.5'1, the fea-
`ture value is the standard deviation & e , defined as
`
`number.of pixels in Si and Pie is the mean of pixel
`values of F,0(z,y) in Si. The standard deviation of
`each sector in each of the eight filtered images de-
`fine the components of our feature vector. The 640-
`dimensional feature vectors (Fingercodes) for finger-
`print images of two different fingers are shown as gray
`level images with eight disks, each disk corresponding
`to one filtered image (Figure 7). The gray level in a
`sector in a disk represents the feature value for that
`sector in the corresponding filtered image. Note that
`Figures 7(a) and (b) appear to be visually similar as
`
`191
`
`(el
`
`ff)
`
`Figure 6: Normalized, filtered (only four orientations
`shown), and reconstructed fingerprint images. Only
`the central part of the fingerprint is shown. (a) area
`of interest (b) normalized image (c)-(f) 0", 45", go",
`and 135" filtered images; (g), (h) reconstructed images
`using four and eight orientation filters, respectively.
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`are Figures 7(c) and (d), but the corresponding disks
`for two different fingers look very different.
`
`f30” rotation. These images are not as noisy as the
`inked fingerprints but they do contain large nonlinear
`deformations. We have not used any of the standard
`databases (e.g., NIST 9) because the inked fingerprint
`images are not representative of the livescan images
`used in the civilian applications.
`Each fingerprint in the database is matched with
`all the other fingerprints in the database. A matching
`is labeled correct if the matched pair is from identi-
`cal finger and incorrect otherwise. A total of 62,250
`(250 x 249) matchings were performed. The distri-
`bution for genuine (authentic) matches was estimated
`with 2,250 (250 x 9) matches and the imposter distri-
`bution was estimated with 60,000 (250 x 240) matches.
`None of the genuine matching scores was zero; the im-
`ages from the same finger did not yield an identical
`Fingercode because of rotation and inconsistency in
`reference location. Given a matching distance thresh-
`old, the genuine acceptance rate is the fraction of times
`the system correctly identifies two fingerprints repre-
`senting the same finger. Similarly, false acceptance
`rate is the fraction of times the system incorrectly
`identifies two fingerprints representing the same fin-
`ger. A Receiver Operating Characteristic (ROC) is
`a plot of Genuine Acceptance Rate against False Ac-
`ceptance Rate for all possible system operating points
`(i.e., matching distance threshold) and measures the
`overall performance of the system. An “ideal” ROC
`curve is a step function at zero False Acceptance
`Rate. Figure 8 compares the ROCs of a state-of-the-
`art minutiae-based matcher [2] with our filter-based
`matcher. Since the ROC curve of the filter-based
`matcher is above the minutiae-based matcher, we con-
`clude that our matcher performs better than a state-
`of-the-art minutiae-based matcher on this database.
`
`Figure 7: Examples of 640-dimensional feature vec-
`tors. (a) First impression of finger 1; (b) Second im-
`pression of finger 1; (c) First impression of finger 2;
`and (d) Second impression of finger 2.
`
`3 Matching and Experimental Results
`Our preliminary experiments involve a database
`containing 250 images (size = 640 x 480) from 25 dif-
`ferent fingers. Ten impressions of each finger are avail-
`able for a total of 250 images. The images were cap-
`tured with an optical scanner manufactured by Dig-
`ital Biometrics. We have developed a GUI to help
`the subjects in the proper placement of their fingers.
`The subjects were asked to place their fingers upright
`at the center of the glass platen and they were asked
`to provide different impressions of their fingers within
`
`Figure 8: Receiver Operating Characteristic (ROC)
`curve.
`An added advantage of an “independent” finger-
`
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`print representation such as that proposed here is that
`it captures discriminatory information that is comple-
`mentary to the information used by commonly used
`minutiae-based fingerprint matchers. Consequently,
`the overall performance of fingerprint matching can
`be significantly improved by combining results of the
`matchers based on different representations. Figure 8
`shows such an improvement in matching accuracy re-
`sults by using a linear combinatinr, of the scores ob-
`tained from the pr<>ljc ~-:d her-based and minutiae-
`based [2] matchers. The weights in the linear com-
`bination were selected corresponding to a line pass-
`ing through origin that best separates the genuine
`distribution and the imposter distribution in a two-
`dimensional plot of scores from the two matchers.
`4 Conclusions
`We have developed a novel filter-based representa-
`tion technique for fingerprint identification. The tech-
`nique exploits both local and global characteristics in
`a fingerprint to make an identification. Each finger-
`print image is filtered in a number of directions and
`a 640-dimensional feature vector is extracted in the
`central region of the fingerprint. The feature vec-
`tor (Fingercode) is compact and requires only 640
`bytes. The matching stage computes the Euclidean
`distance between the template Fingercode and the
`input Fingercode. On a database of 250 fingerprints
`from 25 different fingers, 10 impressions per finger,
`we are able to achieve identification accuracy which is
`slightly better than the performance of a state-of-the-
`art minutiae-based fingerprint matcher. About 99%
`of the total compute time (- 3 seconds on a SUN
`ULTRA 10) is taken by the convolution of the input
`image with 8 Gabor filters. The primary advantage of
`our approach is its computationally attractive match-
`ing/indexing capability. For instance, if the normal-
`ized (for orientation and size) Fingercodes of all the
`enrolled fingerprints are stored as templates, the iden-
`tification effectively involves a “bit” comparison. As
`a result, the identification time would be relatively in-
`sensitive to the database size. Further, our approach
`for feature extraction and matching is more amenable
`to hardware implementation than, say, string based
`fingerprint matcher.
`There are a number of limitations of the initial im-
`plementation described in the paper. The represen-
`tation and matching schemes assume that reference
`frame can be determined with a reasonable accuracy.
`A more realistic approach would consider a combina-
`tion of frame determination methods and then verify
`a consistent frame positioning. The current imple-
`mentation requires that the entire region of interest
`be available and does not take into account occlusion
`
`or obliteration of part of the fingerprint. This sit-
`uation could be remedied by incorporation of “don’t
`care” options for the components of the representation
`which do not correspond to fingerprint area. While the
`present approach tolerates small magnitudes of elas-
`tic distortion and local scaling (due to finger-pressure
`variations), it does not take care of significant non-
`linear elastic distortion in the fingerprints. The inter-
`ridge densities in a fingerprint could be used to obtain
`a canonical representation to compensate for the large
`distortions due to shear and pressure variations caused
`by the contact of the finger with the sensing device.
`We are currently evaluating the performance of the
`filter-based matcher on databases containing thou-
`sands of livescan fingerprints. On a database of 2,672
`images belonging to 167 people, we achieve an FRR
`of 12% for an FAR of 1%. This performance needs to
`be improved significantly. The main problem seems to
`be the failure of reference frame detection algorithm
`in poor quality fingerprint images. We are working on
`(i) a more robust determination of reference frame, (ii)
`refinements of initial strategies for feature extraction
`and matching, and (iii) indexing techniques based on
`the proposed representation.
`References
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`“An Identity Authentication System using Finger-
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