Iris Feature Extraction Using Independent Component Analysis

Abstract

In this paper, we propose a new feature extraction algorithm based on Independent Component Analysis (ICA) for iris recognition. A conventional method based on Gabor wavelets should select the parameters (e.g., spatial location, orientation, and frequency) for fixed bases. We apply ICA to generating optimal basis vectors for the problem of extracting efficient feature vectors which represent iris signals. The basis vectors learned by ICA are localized in both space and frequency like Gabor wavelets. The coefficients of the ICA expansion are used as feature vector. Then, each iris feature vector is encoded into an iris code. Experimental results show that our proposed method has a similar Equal Error Rate (EER) to a conventional method based on Gabor wavelets and two advantages: first, the size of an iris code and the processing time of the feature extraction are significantly reduced; and second, it is possible to estimate the linear transform for feature extraction from the iris signals themselves.