Fusion of Wavelet Features and Gabor Features for SVM-based Iris Verification

Sayan Das; Biswajit Kar

doi:10.52756/ijerr.2024.v43spl.010

Authors

Sayan Das Department of Instrumentation Engineering, Central Institute of Technology Kokrajhar, Assam-783370, India https://orcid.org/0009-0006-4644-9438
Biswajit Kar Department of Instrumentation Engineering, Central Institute of Technology Kokrajhar, Assam-783370, India https://orcid.org/0000-0003-2686-5814

DOI:

https://doi.org/10.52756/ijerr.2024.v43spl.010

Keywords:

Biometrics, DWT multiple wavelets, Gabor filter, Iris feature extraction, Iris verification, SVM with RBF

Abstract

Iris verification now become increasingly prominent in biometric-based person verification systems. It has gained a significant role in biometric systems due to its stability, high uniqueness, contactless and non-invasive properties. Iris has more inherent distinctive features than other biometrics. Feature extraction of iris plays a crucial role in this system for accurate person verification. Using the feature extraction process, unique iris features like textural patterns, crypts, and furrows of iris are extracted. In our study, we did a fusion of Discrete Wavelet Transform (DWT) features with multiple wavelet bases (db4, haar, coif3, and sym4) and Gabor features, which contain a good amount of textural and localized information. Fusion here indicates the concatenation of the extracted features using the above techniques. In this work, we studied this method on the full iris only so that a maximum number of features can be extracted. This combined approach yielded a significant 112 number of features. The extracted features are then verified using a support vector machine (SVM) classifier based on radial basis function (RBF) kernel with training vs testing split ratios of 8:2, 6:4, 4:6 and 2:8. In this study, we achieved the highest overall verification accuracy of 95.9% with training vs testing split ratio of 8:2. For other training vs testing split ratios of 6:4, 4:6 and 2:8 we achieved overall verification accuracies of 91.4%, 93.2% and 91.2% respectively. We got an overall verification accuracy of 92.9%, considering training vs testing ratios of 8:2, 6:4, 4:6 and 2:8.

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