Cross entropy clustering approach to iris segmentation for biometrics purpose

This work presents the step by step tutorial for how to use cross entropy clustering for the iris segmentation. We present the detailed construction of a suitable Gaussian model which best fits for in the case of iris images, and this is the novelty of the proposal approach. The obtained results are promising, both pupil and iris are extracted properly and all the information necessary for human identification and verification can be extracted from the found parts of the iris

Ellipticity and circularity measuring via Kullback-Leibler divergence

Using the Kullback-Leibler divergence we provide a simple statistical measure which uses only the covariance matrix of a given set to verify whether the set is an ellipsoid. Similar measure is provided for verification of circles and balls. The new measure is easily computable, intuitive, and can be applied to higher dimensional data. Experiments have been performed to illustrate that the new measure behaves in natural way

Mahalanobis Distance-Based Algorithm for Ellipse Growing in Iris Preprocessing

Part 4: Pattern Recognition and Image ProcessingInternational audienceWe introduce a new algorithm for ellipse recognition. The approach uses Mahalanobis distance and statistical and analytical properties of circular and elliptical objects. At first stage of the algorithm the starting configuration of initial ellipse is defined. Next we apply a condition which describes how much the shape is ellipse-like on the boundary points.The algorithm can be easily applied to detection of elliptical objects also on grayscale images. Moreover, we discuss the improvement in iris image preprocessing