Abstract
In a large-scale iris-based identification system, iris classification is an important indexing task to reduce the search time in a large database for accurate matching, especially in a de-duplication application. Because of the considerable intra-class variability and small inter-class variability, iris classification is a difficult pattern recognition challenge. In this paper, we propose a novel approach to iris classification based on iris fiber structures. Translation and minor deformation invariant local iris features are extracted using a scattering wavelet network. A simple generative PCA affine classifier is used to classify the resulting invariant feature vectors. Experiments on two benchmark iris databases reveal that the proposed iris classification algorithm is successful and robust in terms of classification accuracy.
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Birajadar, P., Haria, M., Gadre, V. (2023). Scattering Wavelet Network-Based Iris Classification: An Approach to De-duplication. In: Choudrie, J., Mahalle, P., Perumal, T., Joshi, A. (eds) ICT with Intelligent Applications. Smart Innovation, Systems and Technologies, vol 311. Springer, Singapore. https://doi.org/10.1007/978-981-19-3571-8_64
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DOI: https://doi.org/10.1007/978-981-19-3571-8_64
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