Abstract—Single biometric trait for authentication is widely
used in some application areas where security is of high
importance. However, biometric systems are susceptible to
noise, intra-class variation, non-universality and spoof attacks.
Thus, there is need to use algorithms that overcome all these
limitations found in biometric systems. The use of multimodal
biometrics can improve the performance of authentication
system. This study proposed using both fingerprint and face for
authentication in access system. The study integrated
fingerprint and face biometric to improve the performance in
access control system. Fingerprint biometric, this paper
considered restoration of distorted and misaligned fingerprints
caused by environmental noise such as oil, wrinkles, dry skin,
dirt, displacement etc. The noisy, distorted and/or misaligned
fingerprint produced as a 2-D on x-y image, is enhanced and
optimized using a hybrid Modified Gabor Filter-Hierarchal
Structure Check (MGF-HSC) system model. In face biometric
Fast Principal Component Analysis (FPCA) algorithm was used
in which different face conditions (face distortions) such as
lighting, blurriness, pose, head orientation and other conditions
are addressed. The algorithms used improved the quality of
distorted and misaligned fingerprint image. They also improved
the recognition accuracy of distorted face during authentication.
The results obtained showed that the combination of both
fingerprint and face improve the overall performance of
biometric authentication system in access control.
Index Terms—Biometrics, multimodal, authentication, fast
principal component analysis.
The authors are with Tshwane University of Technology, Soshanguve
Campus, South Africa (e-mail:zuvaT@tut.ac.za, esanomobayo@tut.ac.za,
ngwiraSM@tut.ac.za).
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Cite: T. Zuva, O. A. Esan, and S. M. Ngwira, "Hybridization of Bimodal Biometrics for Access Control
Authentication," International Journal of Future Computer and Communication vol. 3, no. 6, pp. 444-451, 2014.
