validity and acceptability

Validity and Acceptability of Results in Fingerprint Scanners

Majid Meghdadi, Saeed Jalilzadeh Computer and Electrical Departments

Zanjan University Iran

Abstract: Fingerprinting has been a traditional way to find and verify the identity of known criminals and terrorists that are wanted and have evaded the law. Fingerprinting can produce a match with an error rating of (+ / -) .001. The security of fingerprint scanners has however been questioned and previous studies have been shown that fingerprint scanners can be fooled with artificial fingerprints, i.e. copies of real fingerprints. The fingerprint systems are evolving and this paper will discuss the situation of today. Two different approaches to the fingerprint scanner area will be covered in this paper. The theoretical approach will discuss live ness detection, i.e. the fingerprint scanners' ability to distinguish between live fingers and artificial clones. Different live ness detection methods will be presented and analyzed with regards to attacks with artificial fingerprints. The empirical approach consists of examining the fingerprint scanners' ability to withstand an attack of an artificial fingerprint using techniques based on earlier researches. The experiments focus on making artificial fingerprints in gelatin from a latent fingerprint. Nine different systems were tested at the Zanjan University and all were deceived. Three other different systems were put up against more extensive tests with three different subjects. All systems were circumvented with all subjects' artificial fingerprints, but with varying results. The results are analyzed and discussed in this paper. The uses of biometric systems are growing every day. Biometrics deals with identifying individuals with help of their biological data. Key Words: Fingerprints – Fingerprint scanners – Sensor attacks – Identification – Feature extraction 1 Introduction Even though the first fingerprint scanners were introduced more than 30 years ago, it is not until the recent years that the interest for fingerprint scanning has increased considerably [1]. With the terrorist attack in New York on September11 2001, the US Government and other governments and organizations, became increasingly interested in the biometrics industry. Passport, border control, and identification cards are areas were fingerprints, as a means of authentication, have become increasingly interesting. The fingerprint scanner market has grown rapidly the last years. With this development, the scanners are shrinking in size, the price is going down, and fingerprint systems are being integrated into electronic equipment such as laptops, mousse, and keyboards [13].

A fingerprint scanner has basically two tasks; to acquire an image of a fingerprint, and to decide whether or not this image matches the image of a previously enrolled fingerprint. The decision phase is done by extracting features from the image and then comparing these features to templates stored in a database. A fingerprint contains a lot of information. Storing and using all this information, would take too much space and unnecessary effort while a lot of the information in fact is redundant. Instead, fingerprint scanners focus on the essential information to make the fingerprint as unique as possible and thus useful in identification and verification situations [2]. This paper will describe the characteristics of a digital fingerprint image; the different scanning techniques used today, the algorithms behind the surface of the scanners, protection schemes, and possible ways of intrusion.

7th WSEAS Int. Conf. on MATHEMATICAL METHODS and COMPUTATIONAL TECHNIQUES IN ELECTRICAL ENGINEERING, Sofia, 27-29/10/05 (pp259-266)

2 Scanning techniques While the first generation scanners used optical techniques, a variety of sensing techniques are used today and almost all of them belong to one of the three families: optical, solid-state, and ultrasound [3].

2.1 Optical sensors The advantages with optical sensors include withstanding temperature fluctuations (to some degree), a fairly low cost, resolutions up to 500 dpi, better image quality and the possibility of larger sensing areas [4, 1]. 2.1.1 Frustrated Total Internal Reflection (FTIR) When you place your finger on an FTIR-based optical sensor (see figure 1), the ridges will be in contact with the prism surface, while the valleys will remain at a distance. One side of the prism is illuminated through a diffuse light (a bank of light-emitting diodes (LED) or a film planar light). The light is reflected at the valleys and randomly scattered (absorbed) at the ridges. The lack of reflection from the ridges makes it possible to acquire an image of the fingerprint. In the early days' FTIR sensors, a CCD camera was used to acquire the fingerprint image. Today, the FTIR sensors have shrunk considerably in size and cost with help of the new CMOS technology. [1, 3] Since FTIR devices sense a three-dimensional surface, it is difficult to fool them with a photograph or image of a fingerprint [1]. Latent prints are however still a problem [5, 6]. Furthermore, it is difficult to make a small enough FTIR device suitable to embed into a PDA or a mobile phone, even though they can be used in mice and keyboards.

Fig 1. An FTIR-based fingerprint sensor [1].

2.1.2 FTIR with a sheet prism This type of optical sensor, use a sheet prism made of a number of "prisms" adjacent to each other, instead of a single large prism (figure 2) With the advantage of size reduction, the quality of the acquired images is however lower than traditional FTIR techniques using glass prisms [1].

Fig 2. A fingerprint sensor using FTIR with a sheet prism. [1]

2.1.3 Optical fibers This technique uses a fiber-optic plate (see figure 3) instead of a prism and lens. The finger is in direct contact with the upper side of the plate, while the lower side of the plate is tightly coupled with a CCD or CMOS camera, which receives the light conveyed through the glass fibers. Since the CCD/CMOS is in direct contact with the plate (without any intermediate lens as in the FTIR techniques), its size has to cover the whole sensing area. High costs will thus be the downside of producing large area sensors with this technique [1].

Fig 3. Fingerprint sensing using optical fibers.

Residual light emitted by the finger, is conveyed through the glass fibers to the CCD/CMOS

camera [1].


2.1.4 Electro-optical These types of sensors consist of two layers: a light-emitting polymer and a photodiode array (see figure 4). When the polymer is polarized with the proper voltage, it emits light that depends on the potential applied on one side. As the ridges touch the surface, and the valleys do not, the potential, and thus also the amount of light, will be different. The photodiode array (embedded in glass) receives the light and generates the digital fingerprint pattern. Some commercial sensors use the light-emitting polymer together with an ordinary lens and CMOS instead of the photodiode array. Images acquired electro-optically, are yet not comparable in quality with FTIR images [1].

Fig 4. Electro-optical fingerprint sensor. [1]

2.2. Solid-state sensors Solid-state sensors (also known as silicon sensors), were first introduced to overcome the problems with size and cost of optical sensors. However, considering a high security device, a large sensing area is needed, and thus the cost will in fact not be any smaller for solid-state sensors than for optical sensors. [1] All silicon sensors consist of an array of pixels, where each pixel is a tiny sensor itself. Four different types of silicon sensing techniques have been proposed to convert the physical information into electrical signals: capacitive, thermal, electric field, and piezoelectric [1]. 2.2.1 capacitive sensors A capacitive sensor consists of a two-dimensional array of micro-capacitor plates embedded in a chip (see figure 5). The finger skin works as the other side of each micro-capacitor plate. This way, variations in electrical charge will appear due to distance variations from a ridge on the fingerprint to the sensor and from a valley on the fingerprint to the sensor.

This small capacitance difference is then used to acquire an image of the fingerprint [3]. Even though being widely used nowadays, capacitive sensors do have a number of disadvantages:

Fig 5. Capacitive fingerprint sensor [1].

♦ Small sensor area: It can be questioned whether or not a small image scan area is enough to accurately identify an individual. The reduction in sensor size does also require more carefully performed enrollments. A poor enrollment may not capture the center of the fingerprint, thus forcing the subsequent identification/verification fingers to be misplaced in the same way. The sensing area can of course be increased, however resulting in a higher cost. [4, 7, 1] ♦ Electrostatic discharge (ESD): Electrostatic discharges from the fingertip can cause large electric fields that could severely damage the device. [1] ♦ Chemical corrosion: The silicon chip needs to be protected from chemical substances (e.g. sodium) that are present in fingerprint perspiration. Protecting the surface with a too thick coating will increase the distance between the pixels and the finger too much and make it more difficult to distinguish between a ridge and a valley. Therefore, the coating must be as thin as possible, yet not too thin, or it will not be resistant to mechanical abrasion [1]. 2.2.2. Thermal sensors Thermal sensors are made of pyro-electric material that generates current based on temperature differentials. The temperature differentials between the skin (the ridges) and the air (in the valleys) are used to acquire the fingerprint image. Since thermal equilibrium is reached quickly, it might be necessary to use a sweeping technique when it comes to thermal


sensors. Thermal sensors are not sensitive to ESD, nor do they have any problems with a thick (10 to 20 microns) protective coating. [1] 2.2.3 Electric field sensors The problems optical and capacitive sensors have with dry skin conditions, calluses, cuts, etc. is not the case of electric field sensors. These sensors enter the skin and create a fingerprint image from below the damaged surface layer. The variations of the electric field are measured in the conductive layer, the boundary between the outer layer of damaged skin and the pristine skin. [8] 2.2.4 Piezoelectric (pressure) The sensor surface is made of a non-conductive dielectric material. When the finger applies pressure, a small amount of current, dependent on the pressure, is generated (this effect is called the piezoelectric effect). The different pressure from the valleys and ridges, therefore result in different amounts of current. One of the disadvantages of this technique, is the materials used, which are often not sensitive enough to detect the differences between ridges and valleys. Additionally, the protective coating blurs the resulting image. [1] 2.3 Ultrasonic sensors In an ultrasonic sensor (see figure 6), a transmitter sends acoustic signals toward the fingertip, and a receiver detects the echo signals, which bounce off the fingerprint surface. The difference in acoustic impedance of the skin (ridges) and the air (valleys) is used to measure the distance, thus acquiring an image of the fingerprint. The frequency range used by these sensors varies from 20 KHz to several GHz. The top frequencies are required to get the required resolution to be able to differentiate fingerprints from each other. [1, 3] It has been stated that the improved image quality from ultrasonic sensors results in accuracy rates approximately a factor of 10 better than any other fingerprint sensing technology on the market today. [7]

Fig 6. An ultrasonic sensor uses sound waves,

which penetrate materials and give a partial echo at each impedance change. [1]

Except electric fields, ultrasound is one of the few technologies that images the subsurface of the finger skin, thus penetrating dirt, grease, etc. on the sensor surface and the finger. Ultrasound technology, though considered perhaps the most accurate of the fingerprint technologies, is not yet widely used due to large size and a quite high cost. Moreover, it takes a few seconds to acquire an image. [4, 1] 3Algorithms in fingerprint scanners A typical fingerprint recognition system (see figure 7) consists of a scanning device (capture and enhancement), a feature extraction part, and a comparison part where an identification/verification decision is taken. This section will shortly describe these different parts in more detail.

Fig 7. Typical structure of a fingerprint recognition system. [2, 9]

3.1 Image enhancement When a fingerprint image is captured, it contains a lot of redundant information. Problems with


scars, too dry or too moist fingers, or incorrect pressure must also be overcome to get an acceptable image. Therefore, a number of filters, some of which will be described below, are applied to the image. [2] ♦Normalization: By normalizing an image, the colors of the image are spread evenly throughout the gray scale. A normalized image is much easier to compare with other images, and the quality of the image is easier determined. [2] ♦Binarization: Making an image binary, transforms the gray scale image into a binary image. Either a global or localized threshold value is used. [2] ♦Low pass filtering: The process of low pass filtering smoothen the image to match the pixels nearby so that no points in the image differ from its surroundings to a great extent. By low pass filtering an image, errors and incorrect data are removed, and it simplifies the acquisition process of patterns or minutiae. [2, 10] ♦Quality markup: Redundant information needs to be removed from the image before further analysis can be performed and specific features of the fingerprint can be extracted. Therefore segmentation, i.e. separating the fingerprint image from the background, is needed. [2, 10] 3-2 Feature extraction and comparison Many algorithms have been developed to match two different fingerprints and they can be divided into the following groups: ♦ Minutiae-based matching: This is the most popular and widely used matching method, partly because it is the same technique as used by fingerprint examiners. A fingerprint pattern is full of minutiae points, which characterize the print. In minutiae-based matching, these points are extracted from the print, stored as sets of points in the two-dimensional plane, and then compared with the same points extracted during the enrollment phase. It is very unlikely that the fingerprint during enrollment and the fingerprint during identification/verification had the exact same angle, horizontal and vertical placement. Therefore, the core point is used as a reference point for the coordinate system and the distance and angle from the core point is calculated and used for each minutiae point. For identification/verification a certain number of minutiae points should match for the user to be successfully logged in. [2, 1]

♦Correlation-based matching: The fingerprint image to be identified is superimposed with the fingerprint image acquired during the enrollment. The correlation between corresponding pixels is computed for different alignments. [1] ♦Ridge feature-based matching: This matching method uses features of the ridge pattern, e.g. local orientation and frequency, ridge shape, and texture information. Even though minutiae-based matching is considered more reliable because of its indistinctness, there are cases where ridge feature-based matching is better to use. In very low-quality fingerprint images, it can be difficult to extract the minutiae points, and using the ridge pattern for matching is then preferred. Ridge feature-based matching can be conceived as a super family of minutiae-based matching and correlation-based matching. [1] 4. The Algorithm The algorithm maps a 2-dimensional fingerprint image to a signal, which represents the gray-level values along the ridges. A pair of consecutive fingerprints is captured in 5 seconds. The last image collected (at time 5 s) is used to determine the location of the ridges, since it usually has darker ridges and better quality. Variations in gray levels in the signal correspond to variations in moisture both statically (in one image) and dynamically. A Fourier transform of the signal (see figure 8) is used to quantify the static variability in gray level along the ridges due to the pores and the presence of perspiration. In particular, the algorithm focuses on frequencies corresponding to the spatial frequency of the pores. Secondly, the dynamic features quantify the change in the local maxima and minima in the ridge signal. [11]

Fig 8. West Virginia perspiration detection

method.


The two plotted lines are the capacitance plots across a ridge of the live finger, measured five seconds apart. The local maximum in the plot corresponds to the pores in the fingerprint ridge that are saturated with moisture. The two sensor readings (solid line=initial reading and dashed line=reading after five seconds), show that the areas between the pores tend to fill up with perspiration over time as the moisture spreads across the ridges. If this tendency cannot be observed, the fingerprint is assumed to be fake. [11, 12] The algorithm develops one static measure and four dynamic measures. Classification can be performed based on each of the individual measures developed. While the individual measures give equal error rates (EERs) of between 5.56 % and 38.89 %, much lower EERs can be achieved by combining all measures. To classify the finger as live or fake/dead, a back-propagation neural network (BPNN) is used with the static measure and dynamic measures as input. [11] 5 Results The results from the experiments will be presented in this section, both in numbers and in percentage. The number of successful logins with the subjects' real fingerprints serves as a control when comparing with the number of false acceptances with the subjects' artificial fingerprints. For each subject, 50 identifications/verifications were performed for each fingerprint scanner, both using their real fingerprints, and using two different copies of their artificial counterparts. The tests with the two different gelatin prints are referred to as round one and round two.

5.1 Round one In the first round of experiments performed with artificial fingerprints, the false acceptance rate (FAR) as another user was 0 % for the Identix fingerprint scanner and the precise fingerprint scanner. There was one occurrence of a false acceptance as another user during the first round, making the FAR as another user about 0.7 % for the Tarsus fingerprint scanner. The FAR with artificial fingerprints during round one, was greater than 0% for all subjects on all fingerprint

scanners except for subject S2 and S3 on the Precise fingerprint scanner. The other FARs with artificial fingerprints varied between 66 % and 100 %, depending on the subject and scanner.

5.2 Round two In the second round of experiments with artificial fingerprints, no false acceptances as other users occurred, making the FAR as another user 0 % for all scanners and subjects. The FARs with artificial fingerprints was greater than 0 % for all subjects and scanners in the second round. Except for the FAR of 12 % for subject S2 on the precise scanner, all FARs were greater than 82 % in the second round. 5.3 Mean values The mean values in percent for all fingerprint scanners and subjects, for real and artificial fingerprints, for both rounds, are shown in figure 9.The mean value of the success rate using real fingerprints was 90%, and the mean values of the FAR with artificial fingerprints were 67% in the first round and 86% in the second round.

Fig 9. Mean values, in percent, for real and artificial fingerprints (both rounds).

6 Conclusion All tested fingerprint systems were defeated with artificial fingerprints. Some systems were easier to fool than others, and some artificial fingerprints were more successful than others. Interesting to notice is that a capacitive, an electric field, and a thermal sweeping sensor were all circumvented with artificial fingerprints. Still, fingerprint recognition


systems can be very useful if used in the right applications under the right circumstances. Depending on the specific application, the users of the system might be able to accept a possibility of intrusion with artificial fingerprints instead of paying a lot extra in terms of money, inconvenience, etc. for a live ness detection/extra means of security that still will not be 100% secure. In another application, the users might demand a very high level of security with high costs, larger acquisition times, more user inconvenience, a higher FRR, etc. It is very important to consider all these factors before starting to use a fingerprint recognition system. Even though it is possible to circumvent a fingerprint scanner with help of an artificial fingerprint, the question can be asked how often this will happen and what the consequences will be. It is very difficult to know how often the attack would take place, but how severe the consequences would be is easier to find out. Live ness detection is definitely a good way to increase the security if it does not increase the costs, FRRs, acquisition time, and user inconvenience to a great extent. Much research is currently being performed in the area of multi-modal biometrics, and it is something that could be more widely used in the future. Otherwise, combining two or more identification/verification methods is a security-increasing method that is widely used in commercial applications. One of the most simple and cheap means of protecting against attacks with artificial fingerprints is by using a verification system with personal smart cards where each user's fingerprint template is stored. An intruder would have to get hold of both the user's smart card and the latent fingerprint. Furthermore, by storing the fingerprint information on the smart card instead of storing it in a central database, another possible attack is removed. If an even higher security level is required, this type of system could also be integrated with a password check. 7 Future works A lot of studies have been performed in the area of attacks with artificial fingerprints on fingerprint scanners. Still, as fingerprint scanners develop, more testing and development of artificial fingerprints is also needed. There have been discussions about integrating

fingerprints into passports and identification cards, using fingerprint recognition systems in border controls, and for airport travel. With regards to how relatively easy it is to fool a fingerprint recognition system with artificial fingerprints, further research is needed before this becomes reality. The following subsections will describe the future work needed in the fields of live ness detection, artificial fingerprints, and fingerprint scanners.

7.1 Live ness detection Many live ness detection methods have been suggested, but few have been tested and evaluated, especially not by third parties. Further development and testing of the perspiration method is currently being performed at the Biomedical Signal Analysis Laboratory at West Virginia University, USA. If this further development and testing is successful, evaluation and testing by a third party is also necessary before the method is used commercially. A big issue with the perspiration method is the acquisition time, which has to be less than today's five seconds.

7.2 Artificial fingerprints Automating parts of the process of creating artificial fingerprints, and simplifying other parts, could be a step towards a mass production of artificial fingerprints. Will it be possible to buy an artificial fingerprint in the future? Coming up with a way to store gelatin artificial fingerprints for a longer time than a week, e.g. by adding a preservative, would increase the use of them. Further research is needed to investigate if it is possible to create artificial fingerprints with pores and a simulation of the perspiration process in fingertips. 7.3 Fingerprint scanners The extensive experiments in this report showed that electric field sensors could be defeated with artificial fingerprints. However, no ultrasound scanners have yet been tested by an independent actor regarding attacks with artificial fingerprints. As more sweeping sensors using different technologies, are being developed, these also need to be tested with regards to attacks with artificial fingerprints.


7.4 Alternative biometrics An alternative approach to further investigation in the biometric area is to investigate which biometric that is really suited to be used in high security applications, e.g. passports, identification cards, and border controls. While fingerprints might be best suited for low security applications, other biometrics might be better to use in other applications. References: [1] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar. Handbook of Fingerprint Recognition. Springer Verlag, New York, NY, USA, June 2003. [2] J. Blomm¶e. Evaluation of biometric security systems against artificial fingers Master's thesis LITH-ISY-EX-3514-2003, Department of Electrical Engineering, LinkÄoping University, LinkÄoping, Sweden, October 2003. [3] T. van der Putte and J. Keuning. Biometrical fingerprint recognition: don't get your fingers burned. In Proceedings of IFIP TC8/WG8.8 Fourth Working Conference on Smart Card Research and Advanced Applications, pages 289{303. Kluwer Academic Publishers, September 2000. [4] International Biometric Group. Optical { silicon { ultrasound, 2004. White paper. Available at http://www.biometricgroup.com/reports/public/reports/finger-scanoptsilult.html [5] A. St¶en, A. Kaseva, and T. Virtanen. Fooling fingerprint scanners – biometric vulnerabilities of the preciseTMbiometrics 100 sc scanner. In 4th Australian Information Warfare and IT Security Conference 2003, Helsinki, Finland, 2003.Telecommunication Software and Multimedia Laboratory, Helsinki University of Technology. [6] L. Thalheim, J. Krissler, and P-M. Ziegler. Body check { biometric access protection

devices and their programs put to the test. c't magazine, 1(11):114, May 2002. [7] Biometric ID. Finger-scan (fingerprints). Available at http://www.biometricid.org/finger.html [8] D. Bursky. Biometric sensor leverages e-fields to improve fingerprint capture. Electronic Design, 1999. [9] T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino. Impact of artificial\gummy" fingers on fingerprint systems. In Proceedings of SPIE Vol. #4677 Optical Security and Counterfeit Deterrence Techniques IV, Yokohama, Japan, January 2002. Yokohama National University. [10] M. Henriksson. Analys av fingeravtryck. Master's thesis LITH-ISY-EX-ET-0239-2002, Department of Electrical Engineering, LinkÄoping University,LinkÄoping, Sweden, June 2002. [11] R. Derakhshani, S. A. C. Schuckers, L. Hornak, and L. O'Gorman. Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners. Pattern Recognition, 36(2):383{396, 2003. [12] Jr. J. D. Woodward, N. M. Orlands, and P. T. Higgins. Biometrics: Identity assurance in the information age. McGraw-Hill/Osborne, Berkeley, California,USA, 2003. [13]Majid Meghdadi, Ali Azarpeivand, Davood Mohammadpoor, Darioush Najafi, Leila Safari, Measurement of Feature Extraction Validity in fingerprint scanners, 2005- Tehran International Congress on Manufacturing Engineering -Iran
