Weighted co-occurrence phase histogram for iris recognition

This paper presents a weighted co-occurrence phase histogram (WCPH) for representing the local characteristics of texture pattern and applies it to iris recognition. We first introduce a weighting function that enables the phase angle of the image gradient at one pixel to contribute smoothly to several adjacent histogram bins. This accounts for the uncertainty of the phase angle estimation brought by disturbing factors such as noise and illumination changes. The weighting function also avoids the quantization problem typical of the traditional histogram. We then define the WCPH by computing the weighted co-occurrence of pairs of image pixels that are at fixed distance. The WCPH models the joint probability distribution of both the phase angle and spatial layout, thus having the potential to capture richer information in texture pattern. Based on the WCPH, we develop an iris recognition algorithm using the Bhattacharyya distance to measure the goodness of match. The recognition algorithm considers the effects of noise and employs a simple image registration scheme to account for image deformation. We evaluate the performance of the proposed work on the UBIRIS.v2 database. We participated in the Noisy Iris Challenge Evaluation-Part II (NICE:II). It evaluates the robustness to noise of iris encoding and matching methods on the UBIRIS.v2 database, where the iris images are captured at-the-distance and on-the-move. We ranked #5 among all the registered participants according to the evaluation of the NICE:II organizing committee.

► We present a weighted cooccurrence phase histogram to characterize the iris texture.
► We introduce a Bhattacharyya distance-based measure for iris image matching.
► An image preprocessing method is developed for accurate iris localization.