Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs

We present algorithms for iris segmentation, feature extraction and selection, and iris pattern matching. To segment the inner boundary from a nonideal iris image, we apply a level set based curve evolution approach using the edge stopping function, and to detect the outer boundary, we employ the curve evolution approach using the regularized Mumford–Shah segmentation model with an energy minimization algorithm. Daubechies wavelet transform (DBWT) is used to extract the textural features, and genetic algorithms (GAs) are deployed to select the subset of informative features by combining the valuable outcomes from the multiple feature selection criteria without compromising the recognition accuracy. To speed up the matching process and to control the misclassification error, we apply a combined approach called the adaptive asymmetrical support vector machines (AASVMs). The parameter values of SVMs are also optimized in order to improve the overall generalization performance. The verification and identification performance of the proposed scheme is validated using the UBIRIS Version 2, the ICE 2005, and the WVU datasets.