IEEE TRANSACTIONS ON SYSTEMS, MAN, AND …€¦ · Time-Series Detection of Perspiration as a ... riety of ﬁngerprint technologies through relatively simple tech- ... Example ﬁngerprint

IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 35, NO. 3, AUGUST 2005 335

Time-Series Detection of Perspiration as a LivenessTest in Fingerprint Devices

Sujan T. V. Parthasaradhi, Reza Derakhshani, Lawrence A. Hornak, Member, IEEE, andStephanie A. C. Schuckers, Member, IEEE

Abstract—Fingerprint scanners may be susceptible to spoofingusing artificial materials, or in the worst case, dismemberedfingers. An anti-spoofing method based on liveness detectionhas been developed for use in fingerprint scanners. This methodquantifies a specific temporal perspiration pattern present infingerprints acquired from live claimants. The enhanced perspi-ration detection algorithm presented here improves our previouswork by including other fingerprint scanner technologies; using alarger, more diverse data set; and a shorter time window. Severalclassification methods were tested in order to separate live andspoof fingerprint images. The dataset included fingerprint imagesfrom 33 live subjects, 33 spoofs created with dental materialand Play-Doh, and fourteen cadaver fingers. Each method hada different performance with respect to each scanner and timewindow. However, all the classifiers achieved approximately 90%classification rate for all scanners, using the reduced time windowand the more comprehensive training and test sets.

Index Terms—Biomedical image processing, biomedical mea-surements, biometrics, fingerprint recognition, liveness, patternrecognition, personal identification.

I. INTRODUCTION

B IOMETRICS can play a vital role in enhancing securitysystems and is under consideration for dramatically in-

creased use in order to minimize security threats in militaryorganizations, government centers, and public places like air-ports. Biometrics systems use physiological or behavioral char-acteristics to automatically determine or verify the identity of aperson. Examples of biometric technologies include fingerprint,facial, iris, hand geometry, voice, and keystroke recognition. Aswith all security measures, a biometric system is subject to var-ious threats including attacks at the sensor level, replay attackson the data communication stream, and attacks on the database[1]. This paper will focus on countermeasures to attacks at thesensor level of fingerprint biometric systems, or spoofing—the

Manuscript received March 1, 2003; revised June 3, 2004. This work wassupported in part by the NSF Center for Identification Technology Research(CITeR).

S. T. V. Parthasaradhi is with Bioscrypt, Inc, Markham, ON L3R 8E3, Canada(e-mail: [email protected]).

R. Derakhshani is with the Department of Computer Science and ElectricalEngineering, University of Missouri, Kansas City, MO 64110 USA (e-mail:[email protected]).

L. A. Hornak is with the Lane Department of Computer Science and ElectricalEngineering, West Virginia University, Morgantown, WV 26506 USA (e-mail:[email protected]).

S. A. C. Schuckers was with the West Virginia University, Morgantown,WV, 26506. She is now with the Department of Electrical and Computer Engi-neering, Clarkson University, Potsdam, NY 13699 USA (e-mail: [email protected]).

Digital Object Identifier 10.1109/TSMCC.2005.848192

process of defeating a biometric system through an introductionof a fake biometric sample or, in the worst case, a dismemberedfinger. Liveness detection, i.e., determination of whether the in-troduced biometric is from a live source, has been suggested asa means to circumvent attacks that use spoof fingers.

II. BACKGROUND

Previous work has shown that it is possible to spoof a va-riety of fingerprint technologies through relatively simple tech-niques. These include utilization of latent fingerprints on thescanner with pressure and/or background materials (e.g., a bagof water), molds created from casts of live fingers, and moldsfrom casts made from latent fingerprints lifted from a surfaceand reproduced with photographing etching techniques [2]–[7].Casts have been made from wax, silicon and plastic, and moldsfrom silicon or gelatin (gummy finger) [4], [5].

Our laboratory has demonstrated vulnerability to spoofingusing dental materials for casts and Play-Doh for molds [6],[7]. Furthermore, we have tested fingerprint scanners with ca-daver fingers. In our testing, ten attempts were performed forall available security levels for optical, capacitive ac, capac-itive dc, and electro-optical technologies [6]. Results showedthat the spoofing rate for cadaver fingers was typically 90%when verified against an enrolled cadaver finger, whereas forPlay-Doh and water-based clay, results varied from 45%–90%and 10%–90%, respectively, when verified against an enrolledlive finger. This research demonstrated that water-based castingmaterials and cadaver fingers are able to be scanned and verifiedfor most fingerprint scanner technologies. Example images fromlive, cadaver, and spoof fingers, obtained using commerciallyavailable fingerprint sensor technologies, are shown in Fig. 1.

In order to avoid spoof attacks of fingerprint biometric sys-tems, various liveness countermeasures have been consideredincluding thermal sensing of finger temperature [8], laser de-tection of the 3-D finger surface and pulse [9], pulse oximetry[8], [10], ECG [8], and impedance and electrical conductivityof the skin (dielectric response) [11]. Other techniques whichcan make spoofing more difficult include challenge response;use of passwords, tokens, and smart cards; and multiple bio-metrics. Summaries of liveness and anti-spoofing methods aregiven in [6], [12], and [13]. Most methods require additionalhardware which is costly and, unless integrated properly, maybe spoofed with an unauthorized live person. In addition, mostpreviously developed methods are not available commerciallyand/or have not been tested rigorously in order to determine theireffectiveness.

1094-6977/$20.00 © 2005 IEEE

336 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 35, NO. 3, AUGUST 2005

Fig. 1. Images captured with commercial fingerprint sensors from live, cadaver, and spoof finger.

Previously, we have developed an anti-spoofing methodwhich is based on a time-series of fingerprint images capturedfrom a dc capacitance-based Si CMOS fingerprint scanner [7].The method uses the physiological process of perspiration todetermine the liveness of a fingerprint. The initial algorithmextracted the grey levels along the ridges to form signals, cal-culated a set of features, and used a neural network to performclassification. The training and test sets were formed from18 live, 18 spoof, and 18 cadaver fingerprints. Results gave100% precision for distinguishing between fingerprints col-lected from live and spoof/cadaver fingers. While these initialresults were encouraging, they also raised a number of issueswhich, if adequately addressed, would aid in the assessment ofthe viability of the approach. These include the performanceof the techniques across a more diverse population, the con-traction of the time series data to achieve user transparency ofthe technique, and the applicability of the approach to otherfingerprint sensor technologies.

In this paper, we present results of the extension of this ini-tial study which addresses these issues. Section III-A discussesthe collection of a larger, more diverse dataset which includes33 live, 33 spoof (based on the 33 live individuals), and 14 ca-daver fingers for each scanner. Section III-B describes the per-spiration detection algorithm which was expanded as part of thiswork to include new features and new classification techniques.The classification techniques used are described in Section III-Cwhile Section IV gives the vitality detection results for optical,electro-optical, and dc capacitive sensor devices for time seriesof 2 and 5 s as well as their statistical analysis. These results arediscussed in Section V, and the emergence from the data of de-vice dependent feature sets are noted as a potential avenue forfurther improvement in this liveness based countermeasure tofingerprint system spoofing.

III. METHODS

A. Data Collection

Three types of fingerprint scanner technologies were usedin this study: capacitive dc (Precise Biometrics, 100 sc),electro-optical (Ethentica, Ethenticator USB 2500), and optical

TABLE INUMBER OF SUBJECTS USED FOR EACH DEVICE AND CATEGORY

(Secugen, EyeD hamster model no HFDUO1A). These systemswere selected based on considerations of technology diversity,availability, and flexibility of the software developer kit (SDK),and ability to readily access and construct a time series of sensorraw images. For each device, fingerprint images were collectedfrom live, spoof, and cadaver fingers. Protocols for data col-lection from the subjects were followed that were approved bythe West Virginia University Institutional Review Board (IRB)(HS#14 517 and HS#15 322). Thirty-three volunteers weresolicited and represented a wide range of ages (20–60 years),ethnicities, and both sexes (17 men and 16 women). The criteriafor data collection protocol was enrollment in 1 of 5 attemptsand the verification in at least 1 of 6 attempts with the enrolledfinger. Also, casts were created from subjects for generationof spoofs (described below). Two live subjects were excludedin two devices and three in another device due to inability toenroll, a technical error, or time constraint. Three subjects inthe spoof category were excluded because a spoof cast was notcreated because of subject time constraints or quality of spoofcast. Table I summarizes the number of subjects used for eachdevice and category. A time-series of 20 fingerprint images wascollected for each subject and device using customized pro-grams developed using manufacturer-provided SDK functions.The images utilized in this paper are the first image and imagesfrom approximately two seconds and five seconds after thestart of the time-series collection. To generate spoof fingerprintimages, finger casts were created from thirty subjects whoparticipated in generation of the time series of live fingerprintimages. Dental impression materials of two types were used:1) name: Aquasil Easy Mix Putty Smart Wetting ImpressionMaterial, content: Quadrafunctional Hydrophilic Addition Re-action Silicone (having very high viscosity, high consistency),

PARTHASARADHI et al.: TIME-SERIES DETECTION OF PERSPIRATION AS A LIVENESS TEST IN FINGERPRINT DEVICES 337

Fig. 2. Example fingerprint images from live (top), spoof (middle), and cadaver (bottom) fingers captured at 0, 2, and 5 seconds (left to right) after placement onthe scanner.

manufacturer: Dentsply Caulk [14] and 2) name: Extrude, con-tent: polyvinylsiloxane impression material (having mediumconsistency-medium bodied), manufacturer: Kerr [15]. Thesedental impression materials formed the cast and Play-Doh wasused to form the mold. A time-series capture of the Play-Dohspoof fingers was captured similar to the live fingers. cadaverfingers (from four subjects, of male age 41, female ages 55,65, and 66) were collected in collaboration with the Mus-culoskeletal Research Center (MSRC) at the West VirginiaUniversity Health Science Center, creating the time-seriesof cadaver fingerprint images. Only fingerprint images from

cadaver fingers which were able to enroll were considered forstudy. Six cadaver fingers were excluded from capacitive dcbecause of failure to enroll. One cadaver finger was excludedfrom the electro-optical device because of technical difficultieswith the scanner (Table I). Examples of the time-series of live,spoof, and cadaver fingerprint images are shown in Fig. 2.

B. Perspiration Detection AlgorithmThe basis for our original method and details of the algorithm

are discussed in detail in [7]. In brief, when in contact with thefingerprint sensor surface, live fingers, as opposed to cadaver or


Fig. 3. Example enlarged fingerprint image sequence which demonstratesprogression of perspiration pattern in time.

spoof, demonstrate a distinctive spatial moisture pattern whichevolves in time due to the physiological perspiration process.Optical, electro-optical, and solid-state fingerprint sensors aresensitive to the skin’s moisture changes on the contacting ridgesof the fingertip skin. These sensors can capture the time de-pendent spatial pattern (Fig. 3). To quantify the perspirationphenomenon, our algorithm maps a 2-dimensional fingerprintimage to a “signal” which represents the gray level values alongthe ridges (Fig. 4). Variations in gray levels in the signal corre-spond to variations in moisture both statically (on one image)and dynamically (difference between consecutive images). Thestatic feature measures periodic variability in gray level alongthe ridges due to the presence of perspiration around the pores.In this measure, the small, highly moisturized areas around theperspiring pores (and not the pores themselves) are detected as asign of active perspiration and thus liveness. The dynamic fea-tures quantify the temporal change of the ridge signal due topropagation of this moisture between pores in the initial imagerelative to image captures 2- (or 5-) s later, where in live fingersthe perspiration levels around the pore areas remain saturatedand almost unchanged while the drier ridge areas between thepores show a rising level of perspiration due to diffusion fromperspiring pores. The cadaver and spoof fingers fail to providethe mentioned static and dynamic patterns due to the lack of ac-tive pore-emanated perspiration.

The basic steps performed in the algorithm are describedas follows. For more information, refer to [7]. First, twofingerprint images are captured within a 2- (or 5-) s interval(referred to as first and last capture). The results are enhancedby having the subjects wipe their fingers immediately beforecapture. Captured fingerprints are preprocessed to remove saltand pepper noise and the hollow pores seen in high-resolutioncaptures since it is the perspiration around the pores and notthe pores themselves that are of interest. A copy of the lastcaptured image is binarized and thinned to locate the ridges.Ridges that are not long enough to cover at least two pores arediscarded. Using the thinned ridge locations as a mask, the graylevels of the original image underneath these ridge paths arerecorded. The resulting signals for the first and the last captureare representative of the moisture level along the ridges for agiven image in the time series. Fig. 4 illustrates these steps byshowing a portion of the ridge signals derived from the first andlast captures from a live source along the mentioned mask.

Prior work established and obtained test results from onestatic and four dynamic measures [7]. The static measure (SM)

uses the Fourier transform of the ridge signal from the firstimage capture and quantifies the existence of active poresthrough the corresponding spatial frequencies. The four dy-namic measures quantify the specific ongoing temporal changesof the ridge signal intensity due to active perspiration. The firstdynamic measure (DM1) is the total swing ratio of the first tolast fingerprint signal. The second dynamic measure (DM2)is the growth ratio of the minimum to maximum of the firstand last fingerprint signal. The third dynamic measure (DM3)is the mean of the differences of the first and last fingerprintsignals, and the fourth dynamic measure (DM4) describes thepercentage change between the standard deviations of the firstand last fingerprint signals.

To increase the robustness of the classification, two additionalmeasures were introduced and are presented here. In the casethat the fingerprint signal swings beyond a device’s dynamicrange (i.e., the device enters cut-off or saturation due to ex-treme dryness/moisture), the information about the minimumsand maximums and their rate of change, utilized in the seconddynamic measure, will be lost. These two measures address thisby taking advantage of the upper and lower cut off region lengthsof the fingerprint signals and converting them into perspirationrates. The equations for the new dynamic measures are givenbelow.

• Dry saturation percentage change: This fifth dynamicmeasure (DM5) indicates how fast the low-cut off regionof the ridge signal is disappearing, thus extracting furtherperspiration rate information from the low cutoff region

(1)

is referring to the ith point (pixel gray level) in thefirst capture ridge signal. is analogous for the secondcapture. i is equal to 1 to the length of the ridge signal (m).m is the same for and since the same mask wasused for and . LT is the low-cutoff threshold of theridge signal . is the discrete delta function. Ahigher DMS value corresponds to faster disappearance ofdry saturation, because the active perspiration raises thebaseline of the ridge signal above the low-cutoff region ofthe sensor. This is an indication of ongoing perspiration.0.1 is added to the denominator to avoid division by zero.

• Wet saturation percentage change: The sixth dynamicmeasure (DM6) indicates how fast the high cutoff regionof the ridge signal is appearing, thus extracting furtherperspiration rate information from the wet-saturationregion

(2)

and are the same as in DM5. HT is the high-cutoff(saturation) threshold of the ridge signal . isthe discrete delta function. A higher DM6 value corre-sponds to faster appearance of moist saturation, becausethe active perspiration raises the baseline of the ridgesignal toward the saturation levels of the sensor. This isan indication of ongoing perspiration. 0.1 is added to thedenominator to avoid division by zero.


Fig. 4. Ridge mask superimposed over the original grayscale fingerprint image (left) and resulting ridge signal for two image captures, 0 (solid) and 5 (dashed)seconds (right).

C. Classification

One static and six dynamic measures are used as features forclassification of images. These measures were obtained fromtwo different time windows of two and five seconds. Classifi-cation was performed separately for each time window. Clas-sification of images is divided into live and spoof where spooffingerprint images include images from Play-Doh spoofs andcadavers. With approximately 75 images for each scanner, 50%of the data was used as a training set and the remaining 50%as the test set for classification. Three classification methodswere used: neural networks, discriminant analysis, and One R.One R and neural network classification was performed usingthe waikato environment for knowledge analysis (WEKA) soft-ware tool [16] that provides different classification techniquesfor large data sets. Discriminant analysis was performed with R[17] and SAS [18].

For neural network classification, a back propagation algo-rithm (with momentum 0.2) was used to train the data set withthe hidden layer of 4 nodes derived from (attributes groups)/2)(where there are seven attributes and two groups). Other specifi-cations include a learning rate of 0.3, a nominal to binary filter,and validation threshold of 20.

Discriminant analysis requires that variables represent anormal distribution. Almost all variables were tested for nor-mality with the help of the R software tool. Discriminantanalysis of two groups was performed using SAS. Discriminantanalysis uses a pooled variance-covariance matrix of variablesfor generating a linear combination of variables called thediscriminant function [19]. The discriminate function separatesthe two groups. All variables were used as parameters fordiscriminant analysis.

One R is the most simple classification tree method. It uses“one-rule” to form a single level decision tree [20]. The ruletests for each variable and its different values. It enumerateshow frequently each class appears for each value of the variable.Then it determines the most frequent class. It creates the rule and

assigns a class for that particular value of variable. Likewise, itforms different rules for different variable values. It computesthe error rate for each rule on the training data. Finally it selectsthe rule with the smallest error rate to classify the groups. In ourcase, One R classifier with minimum bucket size of 6 was used.It chose the static measure to form a rule for all scanners.

IV. RESULTS

Fig. 5 shows the mean of each feature for live and spoof(which includes both cadaver and Play-Doh fingerprint images)for each device. For some features the mean appears graphicallydifferent between groups. Further exploratory statistical anal-ysis was performed which showed that the means were statis-tically different for DM2 and DM5 for capacitiveDC, SM, DM2, and DM6 for electro-optical, and SM, DM2,and DM5 for optical (as indicated by a ).

Figs. 6–8 present the classification rate for live and spooffingerprints for each device and time window. The capacitiveDC device demonstrates between 80%–93.33% classificationfor live fingers and 80%–95% for spoof fingers, dependingon the method and time window. There is little differencein the results for two seconds as compared to five seconds.For the electro-optical device, 62.5%–93.3% classification isachieved for live and 81%–100% for spoof. There is a modestimprovement in live classification from two to five seconds(62.5%–81.3% to 81.3%–93.3%), with a smaller increasein spoof classification (81%–94.7% to 95.2%–100%). Foroptical, classification ranged from 73.3%–100% for live and85.7%–95.4% for spoof with a small change for live classifica-tion from two to five seconds (73.3%–93.8% to 80%–100%).

V. DISCUSSION AND FUTURE WORK

Detection of liveness has been suggested as a means to makespoofing more difficult. For fingerprint recognition, several live-ness methods including temperature, pulse, pulse oximetry, and


Fig. 5. Mean of each feature for live (dashed) and spoof (solid) for each device, capacitive DC (top), electro-optical (middle), and optical (bottom). � indicatesp < 0:01.

Fig. 6. Classification rates of capacitive DC for live and spoof (2- and 5-s windows) using neural network, One R, and discriminant analysis classificationtechniques.


Fig. 7. Classification rates of electro-optical for live and spoof (2- and 5-s windows) using neural network, One R, and discriminant analysis classificationtechniques.

Fig. 8. Classification rates of optical for live and spoof (2- and 5-s windows) using neural network, One R, and discriminant analysis classification techniques.

electrocardiogram have been suggested [6], [8]–[12]. The diffi-culty with these measurements is that they require hardware inaddition to the fingerprint scanner to capture these liveness fea-tures. This is expensive, bulky, and the liveness technique maybe spoofed with a live finger presented in combination with aspoof. Furthermore, proposed liveness methods have not beenrigorously tested and evaluated in relation to impact on statis-tical measurements like false reject and false accept ratios, useracceptance, universality, and collectability.

The research presented here suggests a new method whichdetects the perspiration process through a time-series of finger-print images measured directly from the scanner itself. Usingimage processing and pattern recognition, fingerprint imagescaptured from live fingers can be separated from those capturedfrom spoof or dismembered fingers. This method relies solelyon the underlying fingerprint scanner with the addition of soft-ware-based image processing and pattern recognition to makethe liveness decision. This method is more difficult to spoof,since the spoof would have to replicate perspiration emanatingfrom the pores and spreading across the ridges. Through thispaper and other published work, this method is being evaluatedin terms of statistical performance and other biometric for itsappropriateness to be used widely in combination with finger-print authentication.

The initial version of the algorithm was performed for a dccapacitance scanner with a five second window for eighteensubjects (ages 20–45) [7]. This study expands this research toconsider: 1) a variety of technologies; 2) a large, more diversedataset; and 3) a shorter time window. First, results demon-strate that using standard classification tools, algorithms can beto separate live and spoof/cadaver fingerprint images for op-tical and electro-optical technologies, in addition to dc capac-itance. Second, in collection of the dataset, a variety of agegroups (11 people between ages 20–30 years, 9 people between30–40, 7 people between 40–50, and 6 people greater than 50),ethnicities (Asian-Indian, Caucasian, Middle Eastern), and ap-proximately equal numbers of men and women were chosen.While in this small dataset it is impossible to consider thesegroups separately, the dataset presents a diverse set of fingersand therefore begins to consider potential problems (dry finger,saturated finger, ridge variations, etc.). Even with this diversity,we were able to achieve approximately 90% classification con-sidering standard pattern recognition algorithms and a commonset of features. Third, the original algorithm utilized a 5-s timewindow to show feasibility of the concept. The results from thispaper demonstrate that a shorter time window of two secondsachieves similar classification results.


The classification performed here used a standard set of sevenfeatures and standard classification routines: neural networks,One R (selection of the best single measure and threshold),and discriminant analysis. Training was performed with imagesfrom 15 live subjects and 23 spoof samples. Training was sep-arate for each device and time window. A device-independentalgorithm was not developed due to the large differences inthe measurements across devices and since the statistical anal-ysis showed different features having relevance for differencedevices. Between the statistical analysis and classification re-sults, a device-specific approach would most likely be the mostsuccessful for classification. That is, different measures havevarying effectiveness for different technologies. The future di-rection of this research will be to further explore the featuresand to develop additional features which provide the best clas-sification for each of the device types.

While this study begins to address some of the limitations ofthe original work, more data is needed for further verify that thisphenomenon is applicable across the population. Potentially,subjects having dry and overly moist fingers may receive a falserejection. But this is a problem in general; i.e., independent ofliveness issue. Therefore for future work, environmental testingwill be necessary to demonstrate applicability to a wide varietyof settings within the range of the typical function of the de-vice. Testing will include collection of fingerprints taken at dif-ferent environmental conditions like hot weather, cold weather,fingers with excessive perspiration, damp and dirty fingers. En-vironmental testing will be performed to verify the robustness ofalgorithm toward the fingerprint images taken at different envi-ronmental conditions. Second, while reasonable classification isachieved for a variety of devices using a common set of features,it is necessary to consider each device separately to expand andfine-tune the features and algorithms for each device. This couldpotentially improve classification performance. Third, featuresare averaged across the entire fingerprint image. Targeting areasof the image that are changing due to perspiration may im-prove the separation of live and spoof measurement. Lastly, inthis method the fingerprint image is converted to a ridge signal.While effective in pinpointing the parts of the image which aremost effected by perspiration, image processing techniques mayprovide enhanced features, particularly considering the entirearea around the pores, and therefore improving classification.

VI. CONCLUSION

This paper describes a unique method to determine livenessthrough measurement of perspiration process in the finger.Results are presented which improve upon past reports bydecreasing the time needed to make the decision and demon-strating its applicability to a variety of fingerprint sensortechnologies. A diverse subject population was tested andapproximately 90% classification rate for all scanners wasachieved. The method is totally software based and no addi-tional hardware is required. Application of this liveness methodcan increase the difficulty of spoof attacks for fingerprintscanners.

REFERENCES

[1] N. K. Ratha, “Enhancing security and privacy in biometrics-based au-thentication systems,” IBM Syst. J., vol. 40, no. 3, pp. 614–634, 2001.

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[4] T. van der Putte and J. Keuning, “Biometrical fingerprint recognition:Don’t get your fingers burned,” in Proc. 4th Working Conf. Smart CardRes. Advanced Appl., 2000, pp. 289–303.

[5] T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino, “Impact ofartificial ‘gummy’ fingers on fingerprint systems,” in Proc. SPIE, vol.4677, Jan. 2002.

[6] S. A. C. Schuckers, “Spoofing and Anti-Spoofing Measures,”, vol. 7,Information Security Tech. Rep., 2002.

[7] R. Derakhshani, S. A. C. Schuckers, L. Hornak, and L. O’Gorman, “De-termination of vitality from a noninvasive biomedical measurement foruse in fingerprint scanners,” Pattern Recognit. J., vol. 36, no. 2, 2003.

[8] D. Osten, H. M. Carim, M. R. Arneson, and B. L. Blan, “Biometric,Personal Authentication System,” U.S. Patent #5 719 950, Feb. 17, 1998.

[9] K. Seifried. Biometrics—What You Need to Know. [Online]http://www.securityportal.com/closet/closet20 010 110.html

[10] P. D. Lapsley, J. A. Less, D. F. Pare Jr., and N. Hoffman, “Anti-FraudBiometric Sensor That Accurately Detects Blood Flow,” U.S. Patent #5737 439, Apr. 7, 1998.

[11] P. Kallo, I. Kiss, A. Podmaniczky, and J. Talosi, “Detector for Recog-nizing the Living Character of a Finger in a Fingerprint RecognizingApparatus,” U.S. Patent #6 175 64, Jan. 16, 2001.

[12] V. Valencia and C. Horn, “Biometric liveness testing,” in Biometrics, J.D. Woodward Jr., N. M. Orlans, and R. T. Higgins, Eds. New York:Osborne McGraw Hill.

[13] Liveness Detection in Biometric Systems http://www.ibgweb.com/re-ports/public/reports/liveness.html [Online]

[14] http://ww.dentsply.com [Online][15] http://www.kerrdental.com [Online][16] http://www.cs.waikato.ac. nz/ml/weka/ [Online][17] http://www.r-project.org [Online][18] SAS Institute Inc., North Carolina.[19] W. R. Dhillon and M. Goldstein, Multivariate Analysis Methods and

Applications. New York: Wiley-Interscience, 1984.[20] I. H. Witten and E. Frank, Data Mining Practical Machine Learning

Tools and Techniques with Java Implementations. New York: MorganKaufmann, 1999.

Sujan T. V. Parthasaradhi was born in Ahmedabad,India. He received the B.S. degree in electronics andcommunications from Jawaharlal Nehru Technolog-ical University, Hyderabad, India and the M.S.E.Edegree from West Virginia University, Morgantown.

Currently, he is an Algorithm Developer withBioscrypt, Inc., Markham, ON. His research interestsinclude classification, pattern recognition, and imageprocessing.

Reza Derakhshani received the Bachelors degree inelectrical engineering from Iran University of Sci-ence and Technology in 1994, and the Masters de-gree in electrical engineering, and the Ph.D. degreein computer engineering from West Virginia Univer-sity, Morgantown, in 1999 and 2004, respectively.

He was an Assistant Professor with the School ofComputing and Engineering, University of Missouri,Kansas City, in 2004. His work has resulted in anumber of peer-reviewed publications as well as twopending US patents. His research interests include

computational intelligence with applications in biometrics and biomedicalsignal analysis.


Lawrence A. Hornak (M’00) received the B.S.degree in physics from Binghamton University, theM.E. degree from Stevens Institute of Technology,Hoboken, NJ, and the Ph.D. degree in electricalengineering from Rutgers University, Piscataway,NJ.

Currently, he is a Professor in the Lane Departmentof Computer Science and Electrical Engineering withWest Virginia University (WVU), Morgantown. Priorto joining WVU in 1991, he spent nine years withAT&T Bell Laboratories, Holmdel, NJ. He directs the

multiuniversity Center for Identification Technology Research (CITeR), an NSFIndustry/University Cooperative Research Center. His research includes pho-tonic microelectromechanical systems (MEMS), semiconductor and integratedoptical devices, and physiological and molecular biometric sensors and systems.He has completed one edited book, several book chapters, and has many journaland conference papers.

Dr. Hornak received a National Science Foundation (NSF) National YoungInvestigator Award in support of his work in mixed technology systems whileat WVU. He is a member of OSA and SPIE.

Stephanie A. C. Schuckers (M’95) received theB.S.E. degree in electrical engineering from theUniversity of Iowa, Ames, in 1992, the M.S.E. andPh.D. degrees in electrical engineering: systemsfrom the University of Michigan, Ann Arbor, in1994 and 1997, respectively.

She was an Assistant Professor with West VirginiaUniversity (WVU) for five years and is currently anassociate professor at Clarkson University, Potsdam,NY. Her research interests are signal processing andpattern recognition for a variety of biomedical appli-

cations, including biometric devices, implantable defibrillators, sudden cardiacdeath, and sudden infant death syndrome. She has published many journal pa-pers, conference papers, and book chapters.

Dr. Schuckers was Region 2 representative to the administrative committeefor the IEEE Engineering Medicine and Biology Society for two years.

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IEEE TRANSACTIONS ON SYSTEMS, MAN, AND …€¦ · Time-Series Detection of Perspiration as a ... riety of ﬁngerprint technologies through relatively simple tech- ... Example ﬁngerprint