Neural-based iterative approach for iris detection in iris recognition systems

The detection of the iris boundaries is considered in the literature as one of the most critical steps in the identification task of the iris recognition systems. In this paper we present an iterative approach to the detection of the iris center and boundaries by using neural networks. The proposed algorithm starts by an initial random point in the input image, then it processes a set of local image properties in a circular region of interest searching for the peculiar transition patterns of the iris boundaries. A trained neural network processes the parameters associated to the extracted boundaries and it estimates the offsets in the vertical and horizontal axis with respect to the estimated center. The coordinates of the starting point are then updated with the processed offsets. The steps are then iterated for a fixed number of epochs, producing an iterative refinements of the coordinates of the pupils center and its boundaries. Experiments showed that the method is feasible and it can be exploited even in non-ideal operative condition of iris recognition biometric systems.

[1]  Libor Masek,et al.  MATLAB Source Code for a Biometric Identification System Based on Iris Patterns , 2003 .

[2]  Fabio Scotti,et al.  Noisy iris segmentation with boundary regularization and reflections removal , 2010, Image Vis. Comput..

[3]  A. Ross,et al.  Segmenting Non-Ideal Irises Using Geodesic Active Contours , 2006, 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference.

[4]  Ashok A. Ghatol,et al.  Iris recognition: an emerging biometric technology , 2007 .

[5]  Rafael C. González,et al.  Digital image processing, 3rd Edition , 2008 .

[6]  Pengfei Shi,et al.  A new segmentation approach for iris recognition based on hand-held capture device , 2007, Pattern Recognit..

[7]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  John Daugman,et al.  New Methods in Iris Recognition , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Randy P. Broussard,et al.  Using Artificial Neural Networks and Feature Saliency Techniques for Improved Iris Segmentation , 2007, 2007 International Joint Conference on Neural Networks.

[10]  R.W. Ives,et al.  Iris Segmentation for Recognition using Local Statistics , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[11]  Boualem Boashash,et al.  A human identification technique using images of the iris and wavelet transform , 1998, IEEE Trans. Signal Process..

[12]  David Zhang,et al.  Accurate iris segmentation based on novel reflection and eyelash detection model , 2001, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489).