Performance of Fingerprint Recognition System in Maritime Environment

Karan BondaDepartment of Computer Science

University of California San Diegokbonda@ucsd.edu

Hourieh FakourfarDepartment of Electrical EngineeringUniversity of California, San Diegohttp://sdcc13.ucsd.edu/˜hfakourf

Abstract

This project aims to assess the effects of maritime en-vironment (e.g. sea water, fresh water, etc) on the perfor-mance of fingerprint recognition systems. For the purposeof this project, we obtained two kinds of sensors that willperform the data acquisition using two separate methods.To conduct a series of experimental trials, we recruited vol-unteers to contribute their fingerprints to the dataset. Oncethe dataset is filled with both fingerprints under normal andwrinkled conditions, we use a fingerprint verification sys-tem to retrieve the match score between all combinations ofprints.Finally, we use a graphical plot, receiver operatingcharacteristic (ROC) curve, to illustrate the final result.

1. IntroductionSince 9/11, the federal government and other govern-

ment agencies have turned to biometric for security assur-ance. Fingerprint scanners are among the leading candidatetechnologies for many security applications. In spite of therecent optical improvements in fingerprint sensors, there arestill major issues jeopardizing the reliability of such sys-tems. For instance, the issues involved in fingerprint match-ing are translation, rotation, distortion, and skin conditions(e.g. wrinkles, moisture, and dryness). Thus far, there hasbeen no scientific measure that evaluates the effects of theseissues on the performance of biometric technologies. Dueto the crucial role of biometric devices and their reliabilityin security and defense applications, we decided to performa scientific analysis that aims to assess the effects of mar-itime environment (e.g. sea water, fresh water, etc) on theperformance of the existing fingerprint recognition systems.In particular, we will evaluate the impacts of pruning onfingerprint authentication systems. Pruning is a temporaryskin condition due to prolonged exposure to water. A wrin-kled finger caused by pruning is often referred to as pruneyfingers or water aging [1]. Keratin is the best explanationfor water absorption by skin tissues. Keratin is a protein inthe outer layers of skin (epidermis) [2]. As one immerses

Figure 1. Example of a wrinkled finger after a warm bath

his finger in water, Keratin absorbs water allowing the skinlayers to swell, creating a larger surface area on the surfacethat forces the skin to wrinkle. Figure 1 illustrates an imageof a water aged finger after a warm bath. For the purposeof this study, we chose a number of preexisting fingerprintrecognition systems and treat them as a black box. For dataacquisition, we obtained two fingerprint scanners with twodifferent optical technologies. To acquire fingerprint sam-ples, we recruited volunteers to contribute their fingerprintsto the dataset. After collecting enough samples of both nor-mal and pruney fingers, we use a fingerprint verificationsystem to compute the match score between every possi-ble combination of prints in our database. After computingthe match score, we use a graphical plot (e.g. histograms orROC curves) to assess the effects of pruning on fingerprintrecognition systems. Figure 2 gives a clear overview of thisstudy.

Figure 2. A block diagram of project overview

1

1.1. Hypothesis

Initially, we assumed that fingerprint images of a nor-mal (dry) finger and the prints of a wrinkled finger wouldnot differ greatly, especially in the case of using a scannerequipped with a high end fingerprint multispectral imaging(MSI) technology. Our hypothesis was that once the fingeris pressed onto the platen, the surface of fingertip is flat-tened, so the scanned image of the pruney finger is expectedto be the same as that of the normal finger, unless the skin isstill moist or has water residue. Based on this assumption,we designed different experimental trials and analyze theROC curves for series of different experiments. The resultof these examination will be thoroughly discussed in othersections.

2. Data Acquisition

To acquire samples of pruney fingers, we obtained twokinds of sensors: Sensor A and Sensor B. Sensor A isequipped with a multispectral imaging (MSI) technologywhereas Sensor B relies on the phenomenon of total inter-nal reflectance (TIR). There are numbers of different issuesthat makes Sensor A superior to Sensor B. Like most ofother conventional optical sensors, Sensor B employ TIRimager that contingent upon the orientation of light sourceand the critical angel conditions, whereas the orientation ofthe light source of multispectral imagers do not exceed anycritical angle conditions. Another significant quality of Sen-sor A is its ability to produce high quality images. Compar-ing the scanned prints of the two sensors, fingerprint imagesby Sensor B have lower resolution. An example of imageacquired by Sensor B is demonstrated in figure 3.

Figure 3. SensorB in action.

Lastly, the SDK provided by Sensor A allows one toview and modify fingerprint images with any programwhereas the images stored by SDK of Sensor B are all inac-cessible in other programs. Thereby, Sensor B has superiorqualities in comparison; hence, we chose it to be our pri-mary fingerprint reader.

3. Experimental ProcedureWe will first register the fingerprint of each volunteer un-

der normal conditions, to use as a template for matchingeach of the wrinkled prints. These template files will benamed uniquely so that the analysis of our results can re-fer to which prints were under normal conditions and whichwere not. Once the normal fingerprints are collected, wewill collect the pruney fingerprint samples from the volun-teers. Both of these fingerprints will be collected from theright index finger.

Figure 4. SensorB in action.

We initially collected fingerprints from the left thumbprint, however collecting these prints were troublesome be-cause the orientation of the thumb on the scanner platenvaried from each individual; whereas the placement of theindex finger proved to result in better prints. However, wehave kept the thumb prints of the volunteers in the data set toassess how the algorithms results can differ between printsfrom different fingers. We took samples of the pruney fin-gers when the finger was still wet and also when they weredried. These wrinkled prints were collected from both ex-perimental procedures, when the water was at room tem-perature and when it was warm. Once all of the fingerprintsare collected, we set up a system for our database to differwhich prints were collected when wrinkled versus normal.Figure 4 illustrates the template buckets where each bucket

Figure 5. SensorB in action.

represents an individual participating in the experiment.Thus, each bucket will have a template fingerprint scan andalso a series of pruned fingerprint scan. The diagram infigure 5 depicts the score buckets for our results. We willtest each fingerprint scan within the same bucket with thetemplate (the normal finger scan) and record the score andput it into the genuine bucket. Then we will test a templatefrom one bucket with various combination of scans fromother buckets (so A.template will be tested with B.template,

Figure 6. Histogram of Genuine vs. Impostor scores for both dry and wet prints.

B.pruned1, B.pruned2, ..., H.template, H.pruned1,...) andthe result of this should be put into the impostor bucket.Once the database is set with the appropriate labels and bins,we can test the prints using our minutiae based finger printmatching algorithm. Unlike correlation based algorithms, aminutiae based algorithm consists of two parts. First the al-gorithm will extract minutiae from the fingerprint and createa template file. This process is done for both of the imagesthat we are trying to match. Once the templates are created,the algorithm will run the matching algorithm on the twotemplates to determine a match score based on the numberof minutiae that were matched between the two templates.We ran the test matching for every possible combinationof prints (i.e. both normal and wet print), excluding thescores from a match between a print and itself. The dataconsisted of a 73x73 matrix, which contained the score be-tween any print [i,j]. We sorted the data to set aside genuinescores and impostor scores. Genuine scores were the scoresfrom matches between valid fingerprints of the same indi-vidual. The impostor scores were attained from the matchesbetween two people.

Figure 7. ROC curve of genuine vesus imposture match scores

We will then generate histograms consisting of the data

that we would like to understand, such as the performanceof the fingerprint algorithm when only pruned fingerprintswere used, either when they were wet or dried. Then we cangenerate an ROC curve with the results that were attained.With this graph we can then conclude how pruning affectsthe fingerprint recognition system.

4. ResultUpon running the algorithm on the entire data set, in-

cluding both wet and dry finger prints, we achieved our ini-tial results. As the ROC curve in figure 7 and the histogramin figure 8 indicate, the algorithm does really well under thefactors of water aging. There is a clear distinction betweenthe scores of genuine versus those of the impostors. TheROC curve for the result also indicates that minutiae basedalgorithms overcomes the factor of water aging. Our nexttests were with two separate data sets, one of which is justdry prints, and the other just wet prints. The histogramsfor these two experiments confirms our hypothesis that wa-ter aging does not significantly impact the performance ofminutiae based algorithms.

Figure 8. Histogram plot of genuine versus imposture match scores

In the test trial with just wet prints, we were assumingto see some variation in the impostor and genuine scores,believing that the image quality of the wet prints is not asgood as those of the dry prints. However, the histogram plotin figure 6 indicates that the different quality did not deterthe algorithm from matching the minutiae. Thus our initial

hypothesis is confirmed with the results we achieved.

5. Future WorkAs mentioned above, we have generated the several dif-

ferent experiments acquiring the histogram and ROC for thedatabase. In future, we will be collecting more finger printsunder the water aged conditions as more images will resultin more concrete results. To acquire more data, especiallywith water aged finger prints, we may recruit students inswim team to contribute prints of their pruney fingers to ourdataset.

6. References1. Lin Hong, Anil K. Jain, Sharath Pankanti, Ruud M. Bolle:

Identity Authentication Using Fingerprints. AVBPA 1997:103-110

2. K. Rowe, Robert, and A. Nixon, Kristin. Fingerprint En-hancement Using a Multispectral Sensor. Biometric Tech-nology for Human Identification II. ed. K. Jain, Anil and K.Ratha, Nalini. Proc. of SPIE, Vol. 5779, 2005.

3. Fawcett, Tom. An introduction to ROC analysis. PatternRecognition Letters 27 (2006) 861874

4. N. TSAI and S. KIRKHAM. Fingertip Skin Wrinkling theEffect of Varying Tonicity. Journal of Hand Surgery (Britishand European Volume).2005; 30: 273-27