Multimodal biometric system based face-iris feature level fusion
Автор: Muthana H. Hamd, Marwa Y. Mohammed
Журнал: International Journal of Modern Education and Computer Science @ijmecs
Статья в выпуске: 5 vol.11, 2019 года.
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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.
Face-iris biometric, Fourier descriptors, Fusion, GLCM, LBP, PCA
Короткий адрес: https://sciup.org/15016847
IDR: 15016847 | DOI: 10.5815/ijmecs.2019.05.01
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