A Comparative Study of Feature Extraction and Classification Methods for Iris Recognition

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Provided by: International Journal of Computer Applications
Topic: Security
Format: PDF
Iris recognition is one of commonly employed biometric for personal recognition. In this paper, Single Value Decomposition (SVD), Automatic Feature Extraction (AFE), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used to extract the iris feature from a pattern named iris pattern based on the iris image. The iris patterns are classified using a feed forward Back-Propagation Neural Network (BPNN) and Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel with different dimensions and a comparative study is carried out.
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