Abstract—In this paper, the authors present a hybrid multi- biometric authentication person system that integrates both multi modal and multi algorithmic. Multi-modal, the system using face and fingerprint features, has long been considered common in personal authentication. Multi-algorithm is the system which uses Circularly Orthogonal Moments, such as Zernike Moment (ZM), Pseudo Zernike Moment (PZM), Polar Cosine Transform (PCT) and Radial Basis Function (RBF) Neural Networks. These moments are widely used because their magnitudes are invariant to image rotation, scaling and noise. With such incorporation of multi-modal and multi- algorithms, our proposed system is expected to minimize the possibility of forge in authentication better than uni-biometric systems. In reference to this expectation, the experimental results have demonstrated that our method can assure a higher level of forge resistance than that of the systems using single biometric traits. Index Terms—Multi-biometrics, Personal Authentication, Face, Fingerprint, Circularly Orthogonal Moments. I. INTRODUCTION Biometrics refers to automatic identification of a person based on his physiological or behavioral characteristics [1],[2]. Thus, it is inherently more reliable and more capable of differentiating between an authorized person and a fraudulent imposter [3]. Biometric-based personal authentication systems have gained intensive research interest for the fact they are more secure and more convenient than traditional systems which use passwords, pin numbers, key cards and smart cards [4] in that they can‟t be borrowed, stolen or even forgotten. Currently, there are different biometric techniques either widely-used or under development, including face, facial thermo-grams, fingerprint, hand geometry, hand vein, iris, retinal pattern, signature, and voice-print (Figure 1) [3],[5]. Each of these biometric techniques has its own advantages and disadvantages and hence is admissible, depending on the application domain. However, a proper biometric system to be used in a particular application should possess the following distinguishing traits: uniqueness, stability, collectability, performance, acceptability and forge resistance [6]. Manuscript received May 28, 2012; revised July 04, 2012. Tran Binh Long is with the Department of Computer Science,University of Lac Hong, Dong Nai, 71000 Viet Nam (corresponding author to provide phone: 8490-760-6653; fax: 8461-395-2534;e-mail: tblong@ lhu.edu.vn). Le Hoang Thai is with the Department of Computer Science, Ho Chi Minh University of Science, HCM City, 70000 Viet Nam (e-mail: [email protected]). Fig. 1. Examples of biometric characteristic Most of currently-used biometric systems employ single biometric trait; these systems are called uni-biometric. Despite their considerable advancement in recent years, there are still challenges that negatively influence their resulting performance, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and unacceptable error rates. Some of these restrictions can be lifted by multi-biometric systems [7] which utilize more than one physiological or behavioral characteristic for enrollment and verification/ identification, such as (i) multiple sensors, (ii) multiple representations or multiple algorithms, (iii) multiple instances, (iv)multiple samples, and (v) multiple biometric traits. Those multi-biometric systems can remove some of the drawbacks of the uni-biometric systems by grouping the multiple sources of information [8]. In the first four scenarios, multiple sources of information are derived from the same biometric trait. In the fifth scenario, information is derived from different biometric traits, which gives the system the name of Multimodal. In fact, biometric fusion can also be carried out in any arbitrary combination of the above five sources and such systems can be referred to as hybrid multi-biometric systems [9]. So this system is basically multi-algorithmic as well as multimodal in its design. And it is the focus of our study. Multi-biometric systems are gaining acceptance among designers and practitioners due to (i) their performance superiority over uni-modal systems, and (ii) the admissible and satisfactory improvement of their system speed. Accordingly, it is hypothesized that our employment of multiple modalities (face and fingerprint) and multiple algorithms (ZM, PZM, PCT, RBF) can conquer the limitations of the single modality- based techniques. Under some hypotheses, the combination scheme has proven to be superior in terms of accuracy; nevertheless, practically some precautions need to be taken as Ross and Jain [7] put that multi-biometrics has various levels of fusion, namely sensor level, feature level, matching score level and decision level. In this paper, we proposed a method using hybrid multi- biometrics with decision level fusion. Our work aims at investigating how to combine the features extracted from different modalities. Zernike Moment (ZM)[10] Pseudo Zernike Moment (PZM)[11] and Polar Cosine Transform (PCT)[12] were used to extract both face and fingerprint Hybrid Multi-Biometric Person Authentication System Tran Binh Long, Le Hoang Thai Proceedings of the World Congress on Engineering and Computer Science 2012 Vol I WCECS 2012, October 24-26, 2012, San Francisco, USA ISBN: 978-988-19251-6-9 ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online) WCECS 2012
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Hybrid Multi Biometric Person Authentication System
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Abstract—In this paper, the authors present a hybrid multi-
biometric authentication person system that integrates both
multi modal and multi algorithmic. Multi-modal, the system
using face and fingerprint features, has long been considered
common in personal authentication. Multi-algorithm is the
system which uses Circularly Orthogonal Moments, such as
Zernike Moment (ZM), Pseudo Zernike Moment (PZM), Polar
Cosine Transform (PCT) and Radial Basis Function (RBF)
Neural Networks. These moments are widely used because
their magnitudes are invariant to image rotation, scaling and
noise. With such incorporation of multi-modal and multi-
algorithms, our proposed system is expected to minimize the
possibility of forge in authentication better than uni-biometric
systems. In reference to this expectation, the experimental
results have demonstrated that our method can assure a higher
level of forge resistance than that of the systems using single
biometric traits.
Index Terms—Multi-biometrics, Personal Authentication,
Face, Fingerprint, Circularly Orthogonal Moments.
I. INTRODUCTION
Biometrics refers to automatic identification of a person
based on his physiological or behavioral characteristics
[1],[2]. Thus, it is inherently more reliable and more capable
of differentiating between an authorized person and a
fraudulent imposter [3]. Biometric-based personal
authentication systems have gained intensive research
interest for the fact they are more secure and more
convenient than traditional systems which use passwords,
pin numbers, key cards and smart cards [4] in that they can‟t
be borrowed, stolen or even forgotten. Currently, there are
different biometric techniques either widely-used or under
development, including face, facial thermo-grams,
fingerprint, hand geometry, hand vein, iris, retinal pattern,
signature, and voice-print (Figure 1) [3],[5]. Each of these
biometric techniques has its own advantages and
disadvantages and hence is admissible, depending on the
application domain. However, a proper biometric system to
be used in a particular application should possess the
following distinguishing traits: uniqueness, stability,
collectability, performance, acceptability and forge
resistance [6].
Manuscript received May 28, 2012; revised July 04, 2012.
Tran Binh Long is with the Department of Computer Science,University
of Lac Hong, Dong Nai, 71000 Viet Nam (corresponding author to provide phone: 8490-760-6653; fax: 8461-395-2534;e-mail: tblong@ lhu.edu.vn).
Le Hoang Thai is with the Department of Computer Science, Ho Chi
Minh University of Science, HCM City, 70000 Viet Nam (e-mail: [email protected]).
Fig. 1. Examples of biometric characteristic
Most of currently-used biometric systems employ single
biometric trait; these systems are called uni-biometric.
Despite their considerable advancement in recent years,
there are still challenges that negatively influence their
resulting performance, such as noisy data, restricted degree
of freedom, intra-class variability, non-universality, spoof
attack and unacceptable error rates. Some of these
restrictions can be lifted by multi-biometric systems [7]
which utilize more than one physiological or behavioral
characteristic for enrollment and verification/ identification,
such as (i) multiple sensors, (ii) multiple representations or