International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 11, No. 5, 8 May 2019
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Multimodal Biometric System based Face-Iris Feature Level Fusion
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Author(s)
Index Terms
Face-iris biometric, Fourier descriptors, Fusion, GLCM, LBP, PCA.
Abstract
This paper proposed feature level fusion technique to develop a robust multimodal human identification system. The humane face-iris traits are fused together to improve system accuracy in recognizing 40 persons taken from ORL and CASIA-V1 database. Also, low quality iris images of MMU-1 database are considered in this proposal for further test of recognition accuracy. The face-iris features are extracted using four comparative methods. The texture analysis methods like Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are both gained 100% accuracy rate, while the Principle Component Analysis (PCA) and Fourier Descriptors (FDs) methods achieved 97.5% accuracy rate only.
Cite This Paper
Muthana H. Hamd, Marwa Y. Mohammed, "Multimodal Biometric System based Face-Iris Feature Level Fusion", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.5, pp. 1-9, 2019.DOI: 10.5815/ijmecs.2019.05.01
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