Low-complexity iris recognition method using 2D Gauss-Hermite moments

The authenticity and reliability of iris recognition-based biometric identification system is well-proven. Traditional iris recognition methods use expensive feature extraction algorithms and complex-valued IrisCodes that may hinder the development of a fast identification technique for multimodal biometric system. In this paper, a new set of computationally efficient real-valued features is proposed for recognition of iris patterns using the two dimensional higher-order Gauss-Hermite moments. The IrisCodes generated from the zero-crossings of these moment-based features are capable of capturing hidden nonlinear structures and are potentially invariant to distortions of iris patterns. Experimental results conducted on a generic data set consisting of iris images obtained from two well-known databases show that the proposed method provides encouraging performance. In particular, an acceptable recognition performance in terms of probability of detection for a given false alarm rate may be achieved by the proposed method with a significantly low-level of computational complexity.