SHIFT AND ROTATION INVARIANT IRIS FEATURE EXTRACTION BASED ON NON-SUBSAMPLED CONTOURLET TRANSFORM AND GLCM

摘要

A new feature extraction method for iris recognition in non-subsampled contourlet transform (NSCT) domain is proposed. To extract the features a two-level NSCT, which is a shift-invariant transform, and a rotation-invariant gray level co-occurrence matrix (GLCM) with 3 different orientations are applied on both spatial image and NSCT frequency subbands. The extracted feature set is transformed and normalized to reduce the effect of extreme values in the feature matrix. A set of significant features are selected by using the minimal redundancy and maximal relevance (mRMR) algorithm. Finally the selected feature set is classified using support vector machines (SVMs). The classification results using leave one out cross-validation (LOOCV) on the CASIA iris database, Ver. 1 and Ver.4 show that the proposed method performs at the state-of-the art in the field of iris recognition.