Provided by Cognitive Sciences ePrint Archive
IJCSI International Journal of Computer Science Issues, Vol. 2, 2009
49
ISSN (Online): 1694-0784
ISSN (Print): 1694-0814
An Efficient Secure Multimodal Biometric Fusion Using
Palmprint and Face Image
Nageshkumar.M, Mahesh.PK and M.N. Shanmukha Swamy
1 Department of Electronics and Communication,
J.S.S. research foundation, Mysore University, Mysore-6
2 Department of Electronics and Communication,
J.S.S. research foundation, Mysore University, Mysore-6
3 Department of Electronics and Communication,
J.S.S. research foundation, Mysore University, Mysore-6
Abstract
Biometrics based personal identification is regarded as an
effective method for automatically recognizing, with a high
confidence a person’s identity. A multimodal biometric systems
consolidate the evidence presented by multiple biometric sources
and typically better recognition performance compare to system
based on a single biometric modality. This paper proposes an
authentication method for a multimodal biometric system
identification using two traits i.e. face and palmprint. The
proposed system is designed for application where the training
data contains a face and palmprint. Integrating the palmprint and
face features increases robustness of the person authentication.
The final decision is made by fusion at matching score level
architecture in which features vectors are created independently
for query measures and are then compared to the enrolment
template, which are stored during database preparation.
Multimodal biometric system is developed through fusion of face
and palmprint recognition.
Keywords: Biometrics, multimodal, face, palmprint, fusion
module, matching module, decision module.
1 Introduction
A multimodal biometric authentication, which identifies
an individual person using physiological and/or behavioral
characteristics, such as face, fingerprints, hand geometry,
iris, retina, vein and speech is one of the most attractive
and effective methods. These methods are more reliable
and capable than knowledge-based (e.g. Password) or
token-based (e.g. Key) techniques. Since
biometric features are hardly stolen or forgotten.
However, a single biometric feature sometimes
fails to be exact enough for verifying the identity of a
person. By combining multiple modalities enhanced
performance reliability could be achieved. Due to its
promising applications as well as the theoretical
challenges, multimodal biometric has drawn more and
more attention in recent years [1]. Face and palmprint
multimodal biometrics are advantageous due to the use of
non-invasive and low-cost image acquisition. In this
method we can easily acquire face and palmprint images
using two touchless sensors simultaneously. Existing
studies in this approach [2, 3] employ holistic features for
face representation and results are shown with small data
set that was reported.
Multimodal system also provides anti-spooling
measures by making it difficult for an intruder to spool
multiple biometric traits simultaneously. However, an
integration scheme is required to fuse the information
presented by the individual modalities.
This paper presents a novel fusion strategy for personal
identification using face and palmprint biometrics [8] at
the features level fusion Scheme. The proposed paper
shows that integration of face and palmprint biometrics
can achieve higher performance that may not be possible
using a single biometric indicator alone. This paper
presents a new method called canonical form based on
PCA, which gives better performance and better accuracy
for both traits (face & palmprint).
The rest of this paper is organized as fallows. Section 2
presents the system structure, which is used to increase the
performance of individual biometric trait; multiple
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