Abstract—This paper presents an efficient algorithm for iris
recognition using the Level Set (LS) method and Local Binary
Pattern (LBP). We deploy a Distance Regularized Level Set
(DRLS)-based iris segmentation procedure in which the
regularity of the Level Set (LS) function is intrinsically
maintained during the curve propagation process. The LS
evolution is derived as the gradient flow that minimizes energy
functional with a distance regularization term and an external
energy that drives the motion of the zero LS toward iris
boundary accurately. DRLS also uses relatively large time steps
in the finite difference scheme to reduce the curve propagation
time. The deployed variational model is robust against poor
localization and weak iris/sclera boundaries. Furthermore, we
apply a Modified LBP (MLBP) in an effort to elicit the iris
feature elements. The MLBP combines both the sign and
magnitude features for the improvement of iris texture
classification performance. The identification and verification
performance of the proposed scheme is validated using the
CASIA version 3 interval dataset.
Index Terms—Iris recognition, distance regularized level set,
modified local binary pattern.
The authors are with the Computer Science Department, North Carolina
Agricultural and Technical State University, Greensboro, NC 27411 USA
(e-mail: bpoconno@aggies.ncat.edu, kroy@ncat.edu).
[PDF]
Cite:Brian O’Connor and Kaushik Roy, "Iris Recognition Using Level Set and Local Binary Pattern," International Journal of Computer Theory and Engineering vol. 6, no. 5, pp. 416-420, 2014.
