Effective elliptic fitting for iris normalization

• Published in: Computer vision and image understanding 2013-06, Vol.117 (6), p.732-745
• Main Authors: Lefevre, Thierry, Dorizzi, Bernadette, Garcia-Salicetti, Sonia, Lemperiere, Nadege, Belardi, Stephane
• Format: Article
• Language: English
• Subjects: Active Contours; Algorithms; Applied sciences; Artificial intelligence; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Ellipse; Exact sciences and technology; Fittings; Hough transforms; Iris recognition; Least squares method; Memory and file management (including protection and security); Memory organisation. Data processing; Normalization; Pattern recognition. Digital image processing. Computational geometry; Recognition; Segmentation; Shape; Signal and Image Processing; Software; Variational optimization
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2013.01.005

► Increase in recognition performance achieved with an effective contour fitting scheme. ► Elliptic contour fitting based on Active Contours formulation is described. ► Proposed contour fitting method is not dependent of the segmentation algorithm. ► Results are confirmed on several public databases. Having an accurate parametric description of the iris borders is a critical issue for iris recognition systems based on Daugman’s rubber sheet normalization. Many methods in the literature use very powerful and effective schemes for iris segmentation but often apply a simple estimator procedure, such as the Hough Transform or Least Square Fitting to get this parametric description. Those fitting methods are very sensitive to the segmentation quality as inaccuracies will provoke large errors in the resulting contour. In this article we propose an effective way to find optimal parameters for ellipses in order to proceed the normalization. Our method is based on a variational formulation of the well-known Active Contour techniques leading to a compact formulation for elliptic contours. We show improvements compared to an Elliptic Hough Transform and a Direct Least Square Fitting on the following databases: ICE2005, ND-Iris and Casia-Lamp. We also demonstrate that our scheme can be paired effectively with different segmentation algorithms. Significant improvements of the recognition results were obtained when adding our algorithm after the segmentation stage of VASIR and OSIRIS, two open source packages for iris recognition.