Research Article of Global Journal of Artificial Intelligence
Performance Analysis of Feature Extraction and its Fusion Techniques for Iris Recognition System
Aiyeniko, O, Adekunle, Y.A , Eze, M.O., Alao, O.D, *Ebiesuwa Seun
Department of Computer Science, Babcock University
The extraction of feature shows a significant part of iris recognition system. The robustness of recognition accuracy mostly depends on efficient extraction of feature. In the development of an effective recognition system, it is required that the best discriminating feature available in an iris pattern to be properly extracted. This paper applied some selected feature extraction techniques: 1D Log-Gabor Filter (1D LGF), 2-D Gabor-Filter (2D GF), Discrete Cosine Tansform (DCT) and Scale Invariant Feature Transform (SIFT) for extraction of iris features and fusion technique. The CASIA iris image dataset was used to evaluate with evaluation parameters: False Acceptance Rate (FAR), False Rejection Rate (FRR), Error Rate (RA) and Recognition Accuracy (RA). The combined 1D Log-Gabor and 2D Gabor filter approach outperformed other techniques with 92.22% of recognition accuracy, FRR of 0.0186, FAR of 0.1052 and ER of 2.87%.
Keywords: Feature Extraction, Iris Recognition, 1-D Log Gabor Filter, 2-D Gabor Wavelet Transform, Scale Invariant Feature Transform
How to cite this article:
Aiyeniko, O, Adekunle, Y.A , Eze, M.O., Alao, O.D, Ebiesuwa Seu.Performance Analysis of Feature Extraction and its Fusion Techniques for Iris Recognition System. Global Journal of Artificial Intelligence, 2020; 2:7.
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