Finger Pulse Pattern Recognition as a BiometricLiveness and identity verification based on dynamic physiological features

Jelmer VerkleijUniversity of Twente

P.O. Box 217, 7500AE EnschedeThe Netherlands

j.s.verkleij@student.utwente.nl

ABSTRACTRecognition systems for fingerprints and vascular patternsare being relied upon heavily to provide authentication inautomated systems. However, methods of defrauding im-plementations of these techniques are widespread, whichmakes them a security liability in the protection of high-risk systems. As a means of improving the reliability, atechnique is proposed to detect fraudulent attempts basedon the presence and manifestation of a pulse in the bloodflow. Several experiments are performed to test the capa-bilities and performance of the proposed technique, bothas an additional security measure in existing solutions andas a stand-alone biometric. A potential method of imple-mentation is proposed along with suggestions for furtherresearch and improvements.

KeywordsBiometrics, fingerprint recognition, vascular pattern recog-nition, blood flow, liveness, pulse pattern

1. INTRODUCTIONBiometrics refers to a set of techniques used to identify hu-mans based on natural traits. Two biometric techniquesare in use based on finger traits: fingerprint recognitionand vascular pattern recognition. Fingerprint recognition(the best known and most widely used of the two) gener-ally involves scanning the surface of a finger and inspectingthe unique features of the fingerprint: the pattern of ridgesand valleys, as well as the locations of the minutiae (theend-points of these ridges and valleys). [5, p.1-3] This ap-proach brings with it a significant vulnerability for fraud,since it inspects the fingerprint as an image; anything thatwill resemble the correct fingerprint image will satisfy therecognition mechanism. [3, p.179-180] Improvements existto make forgery harder, for example by creating a 3D im-age instead of a regular 2D image, but methods have beendeveloped to successfully fool even such improved systems.Vascular pattern recognition, a relatively new techniquebased on the personally unique alignment of blood vesselswithin fingers, is generally done by illuminating the fingerwith a certain spectrum of light (and capturing this with aspectrum-specific camera) to create an image of the blood

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vessel alignment, and hence suffers from the same issueswith the still image approach.

There are several applications where this kind of vulnera-bility doesnt pose an actual problem. For example, if thepolice needs to identify a suspect based on fingerprintsfound at a crime scene, someone will actively take im-pressions of a suspects fingerprints and hence know thesource is legitimate. However, when used as a verificationmechanism in an automated environment this is not thecase; there will not always be a person around to check forthe correct presentation of a sample. Especially at loca-tions where biometrics provide a crucial security measureto prevent unauthorized access to the systems, it is veryimportant that the potential for fraud should be mini-mized. Examples of these locations are factories, watertreatment facilities and even nuclear power plants.

An important aspect the current biometric systems havea lot of trouble dealing with is that of liveness: detectingwhether or not the sample used for recognition is providedby living tissue. [11, p.293-295] Several methods have beenproposed, but many appear unviable due to issues witheither reliability or intrusiveness (the degree to which thedetection mechanism poses an inconvenience to the user).If a method can be found that can accurately detect live-ness without being intrusive, biometric security systemscan be made more reliable when it comes to dealing withfraud.

When a biometric system needs to detect the liveness of aprovided sample, it shouldnt just rule out samples com-posed of dead tissue, but also samples provided by inan-imate objects (such as photographs and prosthetics). Infact, the focus lies mainly on that second category, sincemost (if not all) fraudulent samples a biometric systemsdeals with will probably be imitations rather than dead tis-sue. To unequivocally define the samples to be rejected,one should look at the clearly specified category of sam-ples that should be accepted: those that can be reliablydetermined to be composed of living tissue. When sucha detection mechanism can be developed and integratedin an existing fingerprint recognition system, its reliabilityshould improve in rejecting fraudulent samples. Rejectionof fraudulent samples is an important aspect of improv-ing reliability of biometric security measures in high-riskenvironments, where the chance that attempts at foolingthe security mechanisms are significant enough to warrantimproved measures.

2. BLOOD FLOW AS A VERIFICATIONMECHANISM

A generally accepted sign of life used to verify whether ahuman being is alive or dead is the presence of a heartbeat.Although official medical definitions of life and death en-

compass more than just the presence of cardiac activity(and focus more on the concept of brain death [7, ch.2]),this minor difference can be considered insignificant forthe purposes of this research. Verifying the presence ofa heartbeat can be done based on its key manifestation:a pressure wave traveling through the blood resulting inpressure differences along the length of the blood vessels.

Since the goal is to find a detection method that isnt intru-sive, a quick and contactless mechanism is preferable. Anexample of such a simple contactless mechanism is a cam-era. If there is a characteristic that can either confirm ordeny the presence of a pulse, and the manifestation of thischaracteristic is visible to certain cameras, such a charac-teristic would be worth testing as a liveness determinationfactor. Different methods based on blood as a feature ofa live finger already exist (for example by detecting oxy-genation of blood [2, p.442]) but this has shown to haveissues with removing noise [9, p.335-336] and moreoverdoesnt prevent a fraudulent sample from being combinedwith a live finger in order to provide the signal the systemis looking for.

Literature suggests that the pulsing of blood through ar-terioles and capillaries results in minor colour changes. [3,p.191-192] Since blood doesnt flow at a continuous speedbut is governed by a pulse (the heartbeat), there willbe continuous differences in pressureand consequentlyvolumewithin arteries, arterioles and capillaries. Theblood in these capillaries contributes to the skin tone, andtherefore variations in the amount of blood in these capil-laries will result in variations in this tone. However, thereis very little literature to be found regarding attempts atdetecting such colour changes on the skin surface using acamera, so the chances of this working as expected appearslim.

Instead of looking at the manifestation of this on the sur-face, the mechanism can look at the blood vessels them-selves. Research in the field of vein pattern recognitionrelies on the way blood cells absorb and/or reflect lightin the near-infrared spectrum. Hence, instead of usingnatural light, the variations in blood volume can be de-tected using infrared light and cameras. Research on vas-cular pattern recognition is based on hemoglobins highabsorption rates of a certain spectrum of infrared light,best captured by letting the light pass through the finger,i.e. based on transmission instead of reflection. [4, p.4-5]

There are two factors to using blood flow as a verificationmechanism. First, detecting a human pulse correctly willmake it possible to verify the presence of a human fin-ger. Second, depending on the detectable level of detail ina pulse and its manifestation across the fingerwith theconcept of vascular pattern recognition in mindto usethe pattern as a method of (re-)identification. Althoughit might not be reliable enough to function as a separatemechanism, combined with an existing biometric it couldimprove the reliability of such existing mechanisms.

3. ANATOMICAL AND PHYSIOLOGICALBACKGROUND

The arteries and veins in a finger are stacked on top ofeach other (along with nerves) on both the left and rightside of the bones (figure 1, see also [8, p.828]). Thismeans that in a transmission-based setup, significant por-tions of these vessels will be indistuingishable from eachother. Blood runs from the arteries through arterioles,

1Source: New York School of Regional Anesthesia /nysora.com

Figure 1. Transverse section of a finger at theproximal phalanx1

capillaries and venules into the veins. The linear veloc-ity of blood in these vessels ranges from 1.3 cm/s in thearteries to 0.03 cm/s in the capillaries, but the pressurewave created by the pulse travels at a much higher speed,reaching between 10 and 15 m/s in the arteries. [1, p.517]Whereas blood speed decreases as the travelled distanceincreases, the pressure wave speed increases due to thedynamics of the complacent vessel walls [1, p.453-454]:the waves distortion causes the peak to be higher andshorter, causing steeper increases and decreases, makingthe peak more pronounced. Vessels do dampen the pulsewave, causing a more continuous blood flow as the vesselsgrow smaller, with barely any pulse visible in the smallestvessels. Hence, the pulse is mainly visible in the arteriesand arterioles, while only a minor pulse is visible in thecapillaries. [1, p.516] The pressure changes in the aortaduring the cardiac cycle can be found in figure 2, labeledaortic pressure.

Figure 2. Wiggers diagram depicting pressure andvolume changes in and related to the left ventricleoccuring during the cardiac cycle2

An important result of the aforementioned pulse distor-tion occuring in the smaller arteries and arterioles is theappearance of a secondary pulse. This secondary pulse iscaused by diastole, the heart filling with blood (whereasthe primary pulse is caused by systole). Since diastole

2Source: DestinyQx, DavidChangMD / Wikimedia (Cre-ative Commons 2.5 by-sa)

doesnt directly affect the blood in the arterial vessels thispulses amplitude is much smaller. Something to be takeninto consideration is that the complacency of the vesselwalls decreases with age, decreasing the effects of distor-tion of the pressure waves [1, p.518]. Since the secondarypulse mainly becomes visible through this distortion it isto be expected that it becomes less distinguishable withage as well.

It should be noted that the aforementioned velocities donot generally apply to red blood cells within the blood, asthey travel within the fluid through convection. [1, p.517]However, the change in volume caused by the pressurewave does affect these cells, making them an accurate rep-resentation for blood volume (which makes them a propermetric for measuring the pulse). Additionally, note thatwhile venules and veins (the blood vessels that follow cap-illaries in the microcirculation) have pressure waves, theseare not the same as those in the arterial vessels as thesewaves are almost completely dampened by the capillaries.In the finger, these venous pulses will generally be causedby muscle contractions, not the heartbeat [1, p.519]; thismeans that in order to prevent venous pulses influencingthe measurements of the arterial pulses, recordings shouldbe made while the hand is at rest as much as possible.

In order to measure blood volume based on light transmis-sion, the focus needs to be on a wavelength that a commonsubstance in blood absorbs better than other substancesor tissues in the finger near the blood flow in order to beable to distinguish the difference in brightness. A com-mon substance in bloodspecifically, red blood cellsishemoglobin, the protein that serves as an oxygen trans-port mechanism in the blood stream. A technique exists tomeasure oxygen saturation of the blood (oximetry) basedon differences in absorption spectra of hemoglobin bondedwith oxygen (HbO2), hemoglobin bonded with carbon-dioxide (HbCO2) and plain hemoglobin (Hb). [2, p.442]Since this difference is irrelevant for the purpose of thisresearch, a wavelength at which these two react similarlyis preferred.

4. EXPERIMENTAL SETUPInstead of creating a new setup, an existing setup is usedthat was previously built for research on finger vein pat-tern recognition. This setup consists of eight LEDs ar-ranged above the finger (along its length), a surface mir-ror positioned underneath the finger to reflect the lighttowards a CMOS camera fitted with an infrared filter.The exact specifications can be found in the paper by Ton[10, ch.3]. The setup is connected to a PC which canboth control the brightness of the LEDs and capture theimages produced by the camera. The setup works withvalues between 800 nm and 900 nm [10, p.20] at whichthe absorption spectra of Hb, HbCO2 and HbO2 appearvery similar. [6, p.177] The camera captures images with8-bit data per pixel. The measurements of image bright-ness mentioned in this paper will hence range from 0 to255. The captured images have a resolution of 672 by 380pixels.

Four people have their fingers captured: a 19-year-old fe-male, 20-year-old male, a 55-year-old female and a 56-year-old male. For every person, every finger was captured tobuild a complete dataset. In addition to these captures,some fingers were captured multiple times for comparison.All images in this paper depicting captured fingers andvascular patterns are from captures of a single finger ofthe 20-year-old male. Some of the images in this papershow minor adjustments applied by the pattern match-

ing algorithm in order to compensate for relative imagedistortion.

5. DETECTING A PULSEThe image produced by the sensor (figure 3) clearly showsthe fingers vascular pattern. There is no visible bone,andas expectedsmaller vessels like arterioles, capillar-ies and venules cant be distinguished in this image due totheir small size and the high vessel density. Also note theincreased brightness around the phalanx joints, explainedby broader bone structure and tendon tissue (leaving lessroom for blood vessels). [8, p.872]

From the live images produced by the sensor, it becomesvisible that the pulse of the blood flow indeed produces avariation in brightness of the captured image. This vari-ation is best visible in parts of the image where no veinsor arteries appear, c.q. in the fingers center around thephalanx joints and in the tip of the finger, where the imagealmost exclusively shows capillary beds.

Figure 3. A human finger as captured by the setup

In order to analyse the variation in brightness, the imagesare captures for 10 seconds at a capture speed of 30 framesper second. A region in the tip of the finger (where thepulse appeared most visible to the naked eye) is selected,and the average brightness of this region is extracted fromevery frame in the 10-second-capture. When plotting thisdata, is produces a clearly visible waveform reminiscentof a regular arterial pressure waveforms inverse (as seenin figure 4, compared with the aortic pressure from figure2). This inversion can be explained by inspecting the databeing displayed: when the blood pressure rises the bloodvolume will increase, and as a result more light will be ab-sorbed by the blood cells, in turn decreasing the brightnessof the captured image.

Figure 4. Brightness variation in the fingertip

These results are from a capture of a single finger. To seehow other fingers compare, captures are made of all ten

fingers. The fingertip regions of these fingers, when plot-ted alongside each other (in figure 5), show different butcomparably variable values. The captures of other testsubjects are comparable. It should be noted that whilethe index, middle and ring fingers of both hands give com-parable results, the thumbs required increased brightnessfrom the light sources while the little finger required adecrease. Without these lighting adjustments, the imageswere underexposed and overexposed respectively, owing tothe differences in thickness of these fingers. The results ofthese fingers (as depicted in figure 5) are from captureswith readjusted lighting. Placing the thumbs on the setupproved to be difficult due to their length and position-ing on the hand, resulting in slightly different curves thanthose of the other fingers.

Figure 5. Brightness variation in fingertips of allfingers of a single subject (not captured simulta-neously)

To see how well these results compare with those of othersubjects, several captures are plotted alongside each otherin figure 6. In order to prevent cluttering of the figure,only two fingers from each subject are chosen (both in-dex fingers) bringing the total number of plots to 8. Thered and blue plot belong to the younger female and malerespectively, while the purple and green plots show the re-sults for the older female and male subjects respectively.

Interestingly, the secondary pulse is not as distinguishablein the captures of older subjects, as was expected basedon decreased vessel wall complacency. Also note that thecaptures of one subject show a much smaller amplitude;inspection of these captures reveals that these fingers arethinner than the other fingers and hence the image is muchbrighter on average (there is less tissue to absorb the light).Since the tip of the finger appeared overexposed underthese conditions, a region in the middle of the distal pha-lanx was used for these plots (instead of the fingertip),showing a lower average brightness.

The positioning challenges posed by the thumb and thelighting differences applicable to both the thumb and thelittle finger make these fingers difficult to process. Theyare thus considered less favourable fingers to use for pulsedetection. The other fingers compare well and dont showsignificant differences in the detections performance.

6. PULSE PHENOMENAThe results of the previous section show that the pulseis clearly visible in the tip of the finger, but its not theonly region of the image exhibiting brightness variations.To compare these values, two other regions are selected tocompare with the fingertip: one region that shows a vein

Figure 6. Brightness variation in fingertips of leftand right index fingers of four subjects (not cap-tured simultaneously)

or artery, and one region in the joint between the proximaland intermediate phalanx, plotted in figure 8. Althoughnot all waveforms are as pronounced, the pulse is visiblein each of these waveforms. Note that the blue waveformis the same as the plot in figure 4.

Figure 7. Selected regions for comparison ofbrightness variations plotted in figure 8

Figure 8. Brightness variation in selected regions(depicted in figure 7)

The plots show that different regions of the finger pro-duce quite different waveforms. The selected blood vesselproduces a low brightness on average, but the pulse canbe distinguished (albeit with a smaller amplitude). Thephalanx joint produces a small amplitude compared to thehigher average brightness, which can be explained by thelow amount of tissue present in that region (and hence fewvessels to produce a visible pulse).

A similar comparison between regions containing parts ofdifferent blood vessels has been plotted in figure 10. The

Figure 9. Selected regions for comparison ofbrightness variations plotted in figure 10

Figure 10. Brightness variation in selected regions(depicted in figure 9)

smaller amplitude and visibility of the pulse in the arteriesand veins (the visible blood vessels) can be explained byseveral factors. First, the walls of these vessels are thickerthan those of arterioles and capillaries [1, p.454], thus ab-sorbing more light. Second, the ratio of blood volume percaptured area is higher in these vessels than in the cap-illary beds. Third, the vessels are (for a significant part)stacked on top of each other, with only arteries showingthe sought after pulse. All of these factors combined, thegeneral light absorbance is quite high in these areas, mak-ing the difference between highs and lows in brightnessrelatively small. As the waveforms show, there is a pulsevisible, but simply not as significant as in the other areas.

The brightness can also be captured in more abstract re-gions, for example by splitting the image in ten regionshorizontally (figure 12) or alternatively ten regions ver-tically (figure 13). A third option is dividing the imagealong both the horizontal and the vertical axes, producing100 different regions (figure 14).

Figure 11. Selected regions for comparison ofbrightness variations plotted in figures 12, 13 and14

These results show that although the average brightness

Figure 12. Brightness variation in selected regions(marked by vertical lines in figure 11)

Figure 13. Brightness variation in selected regions(marked by horizontal lines in figure 11)

is different in every region, the waveforms have a simi-lar shape. The waveforms for regions of the image notcontaining (parts of) the finger are visibly flat (mostly infigure 13) owing to the lack of a pulse and hence lack ofvariation in brightness. Figure 14 shows that in the var-ious regions there is a clear variety in average brightnessand waveform amplitude, but the shape of the waveform isvery similar. Of course the amount of data and the degreeof overlap makes the figure somewhat chaotic; it mostlyserves to illustrate the visibility of these phenomena, notto present the data in detail.

One particular phenomenon one might expect to appear inthe captures is a visible progression of the pressure wavealong the blood vessels; since the pressure changes dontinstantly occur along the entire length of the blood vesselsbut are caused by a pressure wave which travels throughthe blood, the peak pressure should occur at different mo-ments in different locations. However, analysis of the pre-vious plots reveals that at 30 frames per second, there is nodiscernible difference between the occurences of pressurepeaks in the captures locations. A simple calculation re-veals why this is: since the pressure waves travel throughthe blood vessels at speeds between 10 and 15 m/s in thearteries, and the greatest distance between any two re-gions of the captures is roughly 4 cm, the capture speedshould be at least 250 frames per second in case of a speedof 10 m/s, or an even higher 375 frames per second in caseof 15 m/s. These are minimum speeds; in order to make aclear distinction, even higher speeds are required.

Sadly, the used setup is not powerful enough to reliably

Figure 14. Brightness variation in selected regions(marked by all lines in figure 11)

perform captures at a such short intervals; the maximumcapture speed was 100 frames per second, but capturesmade at this speed also produce little discernible peakdifference and suffer from a lot of noise complicating thepinpointing of the exact peaks. It stands to reason thathigher capture speeds would suffer even more from thisissue, making it a complicated phenomenon to detect re-liably. It should also be noted that, since the main bloodvessels in which this phenomenon should occur (arteries)are largely obscured by veins and produce little detail inthe pulse waveform due to the low average brightness, ahigher capture resolution is required (higher than 8-bit) inaddition to a higher capture speed.

7. PROFILING PULSE PATTERNSThe 100-region plots show that both the average bright-ness and the waves amplitude differ per region. It seemsobvious that these differences are caused by the parts ofthe finger being shown in these regions; a visible artery orvein shows up darker on the image than a region that ex-clusively contains smaller vessels and surrounding tissue,and due to the vessels themselves absorbing light as well asthe blood running through them the detectable variationin brightness is smaller.

Consider that the vascular pattern recognition biometric isbased on the arrangement of blood vessels within a fingerbeing unique per person. Since the differences in pulsemanifestation across the various regions of the image aredependent on this arrangement, it makes sense that theproduced waveforms are also unique per person. As ameans of determining this methods value as a dynamiccomparison metric, additional captures are made of onefinger so that three captures can be compared with eachother.

To see how well this data can be used to make personalprofiles, a scoring system has to be devised. This scoreis defined as the number of regions for which the differ-ence between certain characteristic values is below a giventhreshold. The higher the score, the better the two sam-ples match (according to the chosen comparison character-istic). Naturally, a good comparison characteristic giveshigh scores when comparing two samples from the samefinger, and low scores when comparing samples providedby different fingers.

The first comparison is straightforward: the average differ-ence in brightness of a region is chosen as a characteristic.In other words: for each region, the difference betweenthe captures brightness values is taken for every captured

frame, and for each region the average of this brightnessdifference is taken as a metric. In order to see how well thischaracteristics performs as a comparison metric, a plot ismade with thresholds on one axis and the resulting com-parison score on the other. Comparisons between capturesof the same finger are shown in blue, whereas comparisonsbetween different fingers captures are shown in red.

Figure 15. Scores based on average value differ-ence comparison between captures

Note that although the comparison is based on averagedifferences, this does not make it a comparison based on astill image. This would be the case if for each region the av-erage value is taken, and the averages for both captures arecompared with each other. In essence, such a comparisonis a low-resolution version of vascular pattern recognition,which has proven its value as a biometric. Hence, it makessense that this characteristic shows a steep curve for truepositivesi.e. low thresholds produce high scoresand amore gradual curve for true negatives. Such a comparisonproduces different (though comparable) scores. This canbe considered a second scoring metric.

Figure 16. Scores based on average value compar-ison between captures

A third proposed metric is based on peak values. To beexact, the characteristic compares peak values of both cap-tures, and takes the average difference per region. Sincethe number of peaks can differ between two captures, somepeaks will have to be discarded in this comparison. Notethat this basic comparison does not take the differencebetween the primary and secondary oscillations into ac-count; a more intelligent comparison that does take thisinto account would produce a more accurate metric. Com-paring the previous plots with figure 17 reveals that they

are incredibly similar (although there are, in fact, minordifferences).

Figure 17. Scores based on peak value comparisonbetween captures

All of these metrics produce scoring curves with clearsweet spots, such that a threshold can be chosen whichwill produce true positives and negatives without any falsescores. In contrast, a fourth proposed metric based on theaverage pulse amplitude (comparing averages and peaksfor each region) produces much more ambiguous results,as seen in figure 18. The curves clearly show a lack ofdistinction between positives and negatives; in fact posi-tives produce scores both higher and lower than negatives,making it impossible to pick a threshold with a reasonabletrue positive to false positive ratio.

Figure 18. Scores based on amplitude comparisonbetween captures

It is clear that the amplitude is not a reliable scoring met-ric to distinguish between different fingers, possibly ex-plained by the fact that blood pressure is similar for allhumans within a certain margin of error; a margin largeenough to cause false scores in case of any threshold. Notethat the scale of the horizontal axis in figure 18 is signif-icantly smaller than in the other figures since the curvesare much steeper (owing to the much smaller difference be-tween captures). However, in a more general sense it hasapplication value as a liveness verification metric, distin-guishing fraudulent samples from live samples in general.

Scoring metrics 1, 2 and 3 (figures 15, 16 and 17 respec-tively) show clear differences between matches and non-matches, and the curves are similar enough that the samethresholds can be applied to all of them. Based on this(somewhat limited) dataset, it is possible to set a double

threshold of score(x = 30) 80 and score(x = 40) 90which will result in a perfect result of 100% true positivesand 0% false positives. Additionally, the fourth metric(figure 18) can be used as a liveness verification metricwith a double threshold of e.g. score(x = 10) 60 andscore(x = 15) 85 (or even score(x = 25) = 100 ifscoring a false negative as depicted in figure 18 is deemedacceptable considering noise as a potential cause).

8. IMAGE OBSCURIFICATIONSeveral of the proposed metrics perform well with idealcapture circumstances. Realistically, in case of a finger-print recognition system, an additional layer might beplaced on the finger in order to fool the fingerprint recog-nition with a fake fingerprint. In order to see how well theproposed pulse-based scoring metrics perform in case ofsuch obscurifications, several captures are made of fingerswith paper and plastic placed over the fingerprint. Fig-ure 19 depicts the scoring curves for paper obscurification(in blue), partial paper obscurification (in green), plasticobscurification (in red) and partial plastic obscurification(in purple). The partial obscurifications are captures withonly the top half of the finger obscured (a relevant scenarioto check for since in case of a fingerprint imitation, thatwill most likely be the case). Since the successful scoringmetrics proposed in the previous section are so similar,only one metric is used for these comparisons (specifically,the metric based on average value difference).

Figure 19. Scores based on average value differ-ence comparison between captures (matches in-volving obscurifications)

What becomes immediately clear is that, using the thresh-olds chosen based on unobscured matches, these obscuredimages will not be considered matches with their unob-scured counterparts based on the proposed metrics. How-ever, in case such a layer is used to fool a fingerprint recog-nition system, the finger will most likely not belong tothe person the system tries to match the pattern for. Inother words, what is important is not to see whether ornot the proposed metric produces true positives in case ofobscured images, but more whether it produces true neg-atives. In order to check this, the obscured captures arecompared with captures of various other fingers in figure20.

These plots show that although no true positives are scored,negatives are correctly marked as such by the proposedmetrics even in case of an obscured image being captured.Although not every possible material has been used forthese captures, it is clear that even a seemingly transpar-ent material such as plastic obscures the image in such a

Figure 20. Scores based on average value differ-ence comparison between captures (non-matchesinvolving obscurifications)

way thatwith a correctly chosen thresholdthey tendto be rejected even when the finger is actually a match.On top of that, even if the threshold is to be adjusted,false negatives would turn into true positives without truenegatives turning into false positives. This doesnt makea lower threshold reliable for verification purposes, but itdoes show that despite any obscurifications, the live fingerbeing used is the main factor in determining comparisonscores; both paper and plastic add noise to the image, butdont make it unrecognizeable.

9. VASCULAR PATTERN IMITATIONSThe previous results show that the selected metrics per-form well in case of attempts to defraud a fingerprintrecognition system. However, with an alternative approach,the same mechanism can be applied to vascular patternrecognition systems. The basis lies in the fact that al-though vascular patterns appear more difficult to imitate,theyre still based on still image comparison. This meansthat in theory, an image containing nothing but the vas-cular pattern of a finger could be presented to a recog-nition system which results in the correct pattern beingseen by the system, and hence accepted as a valid sam-ple. Although the previous section already checked forimage obscurification, this particular method deserves itsown checking since is is aimed at a biometric based on thesame principle as the proposed pulse pattern biometric; ifvascular pattern recognition systems can be fooled withthis approach, perhaps the pulse pattern recognition canbe fooled with the same method.

Since the used setup was built as a vascular pattern recog-nition system, a proof-of-concept for defrauding this mech-anism can be tested on this same setup. As a basic test,a fake image is created by tracing the visible lines in aregular finger capture that the system detects as part ofthe vascular pattern. This image is printed with a basiclaser printer on standard office paper, and presented tothe setup.

To the human eye, it is obvious that this presented sampleis a fake. However, the recognition software has no mecha-nism to detect this and simply applies its standard patternrecognition algorithm. As is to be expected, it detects apattern that it compares to regular image captures of boththe same finger and other fingers. As a comparison, someclean images are also compared. The resulting scores arepresented in table 1.

Although the scores of the imitated pattern are not quite

Figure 21. Vascular pattern imitations; tracedlines (top), printout as seen by the setup (mid-dle) and printout along with a live finger as seenby the setup (bottom)

as high as the scores for clean matches, they are also not aslow as the scores for clean non-matches. Considering thisis a crude imitation that was created in barely five minuteswith basic office materials, it seems plausible that a betterimitation technique might deliver even higher scores. Forexample, the pattern extracted by the system contains alot of noise seemingly created by paper fibers; a differentmaterial that does not contain such noise could alreadybe a major improvement. However, since advanced de-frauding of the vascular pattern recognition is beyond thescope of this paper, said improvements arent applied andthe remainder of the research is done based on the crudeimitation.

For the sake of experimentation, it is assumed that a sim-ple image containing the image of a vascular pattern willbe recognized by a simple system based on this biomet-ric. If the proposed pulse-based comparison metrics areadded to such a system in order to improve liveness de-tection, simple images are quickly discarded as they willnot present a proper pulse. But what happens if a regu-

Table 1. Vascular pattern recognition scoresClean image(match)

Clean image(non-match) Pattern imitation

16.33% 9.47% 11.49%28.75% 9.52% 11.83%30.23% 9.66% 11.96%

lar finger is placed on top of the pattern imitation? Sincethe proposed comparison metrics are largely based on av-erage brightness, it seems possible that the brightness ofthe pulse visible to the system is regionally decreased dueto the lines of the pattern imitation absorbing light, po-tentially delivering positive scores. This can be easilytested by placing several different fingers on top of thepresented pattern imitation, capturing the pulse patterns,and comparing them to previously captured pulse patternsof clean finger images.

Figure 22. Scores based on average value differ-ence between captures (involving vascular patternimitation)

In figure 22, three sets of comparisons are plotted. First,the vascular pattern was captured along with the correctfinger, and compared with a clean capture of the samefinger (in red). Second, the vascular pattern was capturedalong with a different finger, and compared with a cleancapture of the same different finger (in green). Third, thevascular pattern was captured along with a different finger,and compared with a clean capture of the finger to whichthe vascular pattern belongs (in blue).

As was the case with the paper obscuring the image in theprevious section (figures 19 and 20), none of the providedsamples deliver positive scores based on the previously ac-cepted thresholds. However, there is a clear difference be-tween the scores of the same finger and those of differentfingers (with one finger being placed on top of the patternimitating the other): similar to the results of the previoussection, if the scoring thresholds were lowered in order todecrease the current 100% rejection rate, the number oftrue positives would still be higher than the number offalse positives, showing that the live finger has more in-fluence on the score than the vascular pattern imitation,getting samples rejected correctly where the vascular pat-tern recognition could result in incorrect acceptance.

10. CONCLUSIONSThe presented pulse-based comparison metrics perform re-markably well. A scoring threshold could be determinedthat delivered an ideal true positive to false positive ratioin comparing clean images (100% versus 0%). Addition-ally, in prototypical applications as an added verificationmechanism for existing biometrics, all fraudulent sampleswere correctly rejected. The fact that no true positiveswere delivered either does not hurt the metrics quality,since realistically that should not be a requirement in sit-uations where an attempt is made to defraud a biometric

system (it seems reasonable to require removal of any ob-scurifications when presenting a finger to a biometric veri-fication system). The index finger, middle finger and ringfinger are considered favourable fingers for providing sam-ples, whereas the thumb and little finger pose a variety ofchallenges when used for this purpose.

Based on these results, a variety of imitation techniquesfor the existing biometrics of fingerprint recognition andvascular pattern recognition can be considered inept. Ev-ery imitation technique that works as a static stand-alonesolution (such as a printed image or simple prosthetic fin-ger) will be rejected since it will lack a pulse, and tech-niques that are combined with a live finger will be rejectedbased on a mismatch in pulse patterns. The amplitude-based scoring metric has shown to be a suitable livenesscheck. Additionally, although no experiments were per-formed based on the principle, checking for basic wave-form features (e.g. the articulation of the secondary pulseor the shape in general as is already done in ECG-basedbiometrics [2, p.433-435]) seems an effective method as aliveness check and potentially an identification check. Ef-fectively, this forces any attempts at defrauding the verifi-cation mechanism to use intricately built prosthetic fingerswith not only a realistic fingerprint and/or vascular pat-tern, but also a physiologically accurate pulse (with cor-rect differences in pulse manifestation in different regionsof the finger).

When extrapolating these limited results, pulse patternrecognition could prove to be a powerful biometric whichwill present major complications for attempts at defraud-ing the verification. In order to prove this conjecture, fur-ther research will be required that will both strengthenthe above conclusions and add additional checking possi-bilities. Until such research has been done, the currentresult set isat a minimumgood reason to assume thistechnique will have a value in a setup combined with otherproven techniques. Advanced verification systems can al-ready be created by combining fingerprint recognition withthe vascular pattern biometric; adding pulse pattern ver-ification makes any existing biometric verification devicesincredibly strong checking mechanisms, both such com-bined biometrics and either biometric separately.

Of course, the sample size has been limited and the possi-bilities of defrauding the proposed verification mechanismhavent been fully explored. There is reason to suspectthat under certain circumstances the proposed thresholdswill not deliver a perfect performance as they did withthe limited samples used for this paper. In order to trulytest the value of this proposed system, additional researchcould be performed exploring its performance when ad-justments are made to the capture method (e.g. reflectioninstead of transmission), samples (e.g. sample size, sub-jects with potentially influential medical conditions) andfraudulent attempts (e.g. proven methods of imitations forfingerprints and vascular patterns). The (as of yet theoret-ical) method of detecting pressure wave progression couldnot be reliably confirmed, but improvements to the setupwill be required to explicitly be able to reject the proposedmethod. Additionally, the possibilities of integrating thisverification mechanism in existing biometric systems basedon fingerprints and/or vascular patterns should be inves-tigated in order to determine its application potential byitself (is it technically feasible to combine these mecha-nisms into a single device?).

Finally, several major factors in the capture environmentneed to be taken into account when interpreting the pre-sented results. First, the matching scores were done based

on 10-second captures at 30 frames per second, effectivelyusing 300 frames for comparison. Such a period of timeas a requirement decreases the user-friendliness dramat-ically (c.q. it presents high intrusiveness). Second, thelighting conditions were kept constant throughout the cap-tures, as was the alignment of the fingers during the cap-tures. When variation of these factors is introduced itcan add high levels of noise. For the proposed techniqueto be useful in a realistic environment, methods need tobe added that will compensate for these factors and pre-vent score distortion. Third, all subjects were relativelyat rest while the captures were made. The physiologicalbackground of venous pulses (caused by muscle activity)suggests images will suffer from noise when different tasksare performed before the captures are taken, or even withbasic temperature changes (considering the body shiversin case of a cold environment).

There is a lot of room for further research, and a lot ofsuch additional research will be required to put the pro-posed mechanism to the test as an applied biometric anddetermine its value in realistic situations. However, basedon the limited results created with basic experiments andsmall datasets, this already seems to be a promising tech-nique that can both function as a stand-alone system andstrengthen other biometrics, improving the reliability ofboth existing and future identity verification security sys-tems.

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