Biometric technologies are very important these days for improving the accuracy of protecting private data from unauthorized access. It helps overcome deficiencies of current security traditional systems. For the last decade, researchers are developing new methodologies that employ biometrics to boost security field. This article proposes effective methods for Iris recognition based on multi-feature fusion. A feature fusion approach is implemented to improve the iris recognition rate. In particular, Haar Wavelet Transformation (HWT) features and principal Component Analysis (PCA) are used to model the iris texture. Both approaches are fused to improve performance. Fusion results are compared to those from each feature alone and with other reported work. The results obtained with the proposed method are better than the currently reported results.
A.M. Hegazy, S., G.M. Mostafa, M., & Abu Elfetouh, A. (2017). Efficient Iris Recognition Using Multi-feature Fusion. Mansoura Journal for Computer and Information Sciences, 13(1), 1-8. doi: 10.21608/mjcis.2017.311791
MLA
Shaimaa A.M. Hegazy; Mostafa G.M. Mostafa; Ahmed Abu Elfetouh. "Efficient Iris Recognition Using Multi-feature Fusion", Mansoura Journal for Computer and Information Sciences, 13, 1, 2017, 1-8. doi: 10.21608/mjcis.2017.311791
HARVARD
A.M. Hegazy, S., G.M. Mostafa, M., Abu Elfetouh, A. (2017). 'Efficient Iris Recognition Using Multi-feature Fusion', Mansoura Journal for Computer and Information Sciences, 13(1), pp. 1-8. doi: 10.21608/mjcis.2017.311791
VANCOUVER
A.M. Hegazy, S., G.M. Mostafa, M., Abu Elfetouh, A. Efficient Iris Recognition Using Multi-feature Fusion. Mansoura Journal for Computer and Information Sciences, 2017; 13(1): 1-8. doi: 10.21608/mjcis.2017.311791
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