Efficient Iris Localization via Optimization Model

Abstract EN

Iris localization is one of the most important processes in iris recognition.

Because of different kinds of noises in iris image, the localization result may be wrong.

Besides this, localization process is time-consuming.

To solve these problems, this paper develops an efficient iris localization algorithm via optimization model.

Firstly, the localization problem is modeled by an optimization model.

Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization.

And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids.

Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids.

Experimental result indicates that the proposed algorithm is efficient and effective.