A New Snake Model for Pupil Localization Using Orthogonal Polynomials Transform

Abstract

Abstract-In this paper a new approach for representing and evolving deformable contour or snake model to accurately detect pupil boundary for improving the performance of iris recognition systems is proposed. The proposed model extracts the boundary with computationally efficient Laplacian of Guassian (LoG) mask. The LoG mask is obtained from the set of polynomial basis operators derived from Orthogonal Polynomials Transform. Two types of controlling force models, introduced as internal and external forces are designed to properly activate the contour and locate it over the pupil boundary. The internal forces are designed to smooth the contour as well as to keep it close to a boundary by pushing the contour vertices towards the boundary of interest. The external forces are responsible for pulling the contour vertices towards the pupil boundary and the contour is deformed into a new shape in response to the effective force. A revised contour estimate of the selected boundary based on the execution of the contour tracing algorithm is generated. The results are highly encouraging to capture the contour of non-circular shaped pupil of iris. Experimental results on the CASIA v1.0 iris database demonstrate that the proposed snake model outperforms in both accuracy and speed.

Iris Edge Extraction and Location by Advanced Watershed and Active Contour Method

“…Although our algorithm was less time-consuming, but our algorithm isn't used to the images with blurred, unfocused condition. Krishnamoorthy 25 proposed a new pupil location method based on snake model, and the speed of his algorithm (computation time is 3.89 ms) is higher than our algorithm. But the size of image he used is unclear, and he didn't segment the sclera boundary.…”

Image Enhancement Methods: A Review

“…The method decomposes the original fingerprint into 3 smaller images corresponding to different frequency bands and contextual filtering was performed by using pyramid levels and 1-D Gaussians. A screen mammogram image denoising and enhancement technique that is based on Orthogonal Polynomial Transformation (OPT) is proposed in [78]. The technique scales a set of OPT edge coefficients to an inverse transformed set to obtain contrast improved image.…”

Pupil Localization by a Template Matching Method

“…In the literature on pupil detection, a first group of works (Boumbarov et al, 2009;Krishnamoorthy and Indradevi, 2010) is based on the technique of active contours. These methods assume that a parametric model of the shape of the contour of the pupil is known (usually an ellipse is used); then they employ different algorithms for the iterative learning of the chosen model parameters.…”

“…These methods assume that a parametric model of the shape of the contour of the pupil is known (usually an ellipse is used); then they employ different algorithms for the iterative learning of the chosen model parameters. Boumbarov et al (Boumbarov et al, 2009) propose the use of a Particle Filter; the other approach works by minimizing an energy function based on the gradient (Krishnamoorthy and Indradevi, 2010).…”