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8/8/2019 Collaborative Fingerprint Authentication by Smart Card and a Trusted Host
Smart card is an important component in e-commerce
security. In aprevious CCECEpaper, we introduced the
idea for verification of the ownership of a smart card
using fingerprint. An owner's fingerprint is registered
into a smart card. When using smart card on a
computer, the card software will match the user's
fingerprint with that stored in the card. This paper
describes the continuation work of this research. Our
goal is to extendthe role ofthe smart card to become an
active authenticator for participation in fingerprint
authentication process.The
heart ofproblem lieson
thelimited computing power of the card's processor. This
paper reports the detailed descriptions of the design,
implementation and experiments.
1 IntroductionSmart card[6], which is a credit card sized plastic card,
embedded with a special type of hardwired logic or a
microprocessor to holtl critical information securely, is a
good choice of light-weighted hardware assisted
cryptographic devices for protection at the client side,when conducting some kinds of online activities, such
as e-commerce[9] on the intemet.
In recent years, there is an increasing trend of using
biometrics information such as eye retina, fingerprint,etc for user authentication in order to strengthen thesecurity measures of different electronic/embeddedsystems, including smart card systems. However, most
of these systems have a common insecure characteristicthat the biometrics authentication process is solely
accomplished out of the smart card processor. For
example, in fingerprint-based card systems, the card
needs to insecurely release the critical fingerprint mastertemplate information into a host computer with anextemal fingerprint reader to perform the fingerprint
matching.
In a previous CCECE paper[8], we introduced the idea
for verification of the ownership of a smart card using
fingerprint. One or more fingerprints of the owners areregistered into smart card. When the owner uses his
smart card on a computer, the card software will attemptto match the user's fingerprint with that stored in the'
card. In this way, the authentication of smart card can be
established. This paper describes the continuation work
Application in Our ProjectIn our work, fingerprint comparison is chosen as the
biometrics authentication tool for its maturity in termsof algorithm availability and hardware feasibility. The
novel technique for fingerprint identification [1],[2],[3]
has been well developed in the field of image
processing. Generally speaking, when we want to
compare two fingerprint images, it is needless to
accomplish this using a pixel-by-pixel methodology. On
the contrary, we can simply compare some pre-extra(:ted
features. In this regard, we have adopted the minutiae
method [1],[2],[3],[4],[5] in our work.
Minutiae refer to the ridge ends and ridge branches of a
fingerprint image. After some ad-hoc minutiae
extraction process [1],[2],[3],[4],[5], we obtain a set of
minutiae which is unique for every person[12]. This
process transforms the fingerprint-matching problem
into a more general point-matching problem. Several
well-known point-pattem-matching algorithms havebeen proposal in the late80's[1l].
We conducted our work using smart card equipped with
a 5 MHz Java processor[7] with no floating-pointarithmetic support. In our previous work, after we had
added a fixed-point arithmetic support to the smart card,
the card processor required about 7-10 seconds to
accomplish the point matching process. One way to cut
down this f i g u r ~ i i s to let the computer hosting the smartcard reader tqcarry out a more substantial share of thecomputational work. Unfortunately, this implies that
more fingerprint data must be transferred out of the
smart card so that data leakage becomes a problem. Inthe following part, we will discuss our continued e1fort
in this direction to enable the matching process becompleted in real time and secure manner.
3 Abstracted .M,:odel of Fingerprint
MatchingIn our recent work, we focus on enhancing the
performance of minutiae matching process on the smart
card. We assume that the process ofminutiae extraction
is done by a fingerprint capture device equipped with a
DSP chip. Before discussing our new algorithm design,
8/8/2019 Collaborative Fingerprint Authentication by Smart Card and a Trusted Host
sample data are consistent with each other. The result is
shown below:
Data Percentage match Percentage matchset computed by Polar computed by
coordinate approach Cartesian coordinateapproach
1 100% 1000/0
2 89% 94%
3 100% 100%
4 100% 100%
5 92% 91%
6 0% 0%
7 100% 100%
8 100% 100%
9 89% 100%
10 100% 100%
Remark: there are about 20 mmutIae In both master and
live template in each data set.
Though the above result shows the consistency of the
two approaches is satisfied, the accuracy greatly
depends on the detennination of the average position
(centroid) which in tum depends on different betweennumber of minutiae extracted from master and live
fingerprint image. Otherwise, the result can deteriorate
significantly
6.2 Time RequirementWe ran the sample data using cartesian co-ordinate
approach and polar co-ordinate approach inside the
smart card. We found that the average time to complete
the cartesian point pattern matching algorithm is about
1.0 second, and the average time to complete the polarpoint pattern matching algorithm is about 0.8 second.
Noted that the above average time does not include the
transfer time of data to smart card. The average data
transfer time is about 2.5 seconds. Therefore, the totaltime for a complete authentication is 3-4 seconds which
is an obvious improvement compare with our last year
result.
'7 Conclusion and FutureWork
In contrast to traditional approach on fingerprint
matching, like string matching[5], our approach issolely based on 2D geometry, which is more suitable tobe run by smart card with limited processing power.
However, the corresponding error tolerance ability willbe weakened. The next phase of this project comes to
requirement analysis of the .image pre-processing and'
feature extraction against noisy minutiae with respect to
smart card basedmatching algorithm.
112
References
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approach to on-line fingerprint verification",proceedings VIII Int. Symp. on Artificial
Intelligence, Mexico, Oct. 1995.
[2] D. Maio, D. Maltoni, "Direct Gray-Scale Minutiae
Detection in Fingerprints", IEEE Transactions on
Pattern Analysis Machine Intelligence, v. 19, no.I, pp. 25-29,1997.
[3] O. Bergengruen, Matching Minutiae of Fingerprint
Images, pp. 5-7 1994
[4] J. D. Stosz, L. A. Alyea, Automated system for
fingerprint authentication using pores and ridge
structure[5] A. Jain, L. Hong, R. Bolle, On-line Fingerprint
Verification, pp. 1-33, 1996
[6] Hendry, Smart Card Security and Applications,
Artech House, Inc., 1997
[7] . http://www.gemplus.com
[8] Y.S. Moon, H.C. Ho, K.L. Ng, "A Secure Smart
Card System with Biometrics Capability"
Proceedingsof
the 1999 IEEE CanadianConference on Electrical and ComputerEngineering, Edmonton, pp. 261-266,May 1999.
[9] Y.S. Moon, H.C. Ho, "Secure Transport Protocol
for E-Commerce - SET versus SSL", inMult imedia Infonnation Systems in Practice,Springer Verlag Press, pp. 389-397, Dec. 1998,
Hong Kong.
[10] P.M. Griffin, C. Alexopoulos, "Point Pattern
Matching Using Centroid Bounding", IEEETransactions on System, Man and Cybernetics,vol. 19, No.5, September/October 1989.
[11] G.S. Cox., G. de Jager., " A Survey on PointPattern Matching and a New Approach to Point
Pattern Recognition", Processing of the 1992
South African Symposium on Communicationsand Signal Processing, pp.243-248, 1992.