Sparsity inspired selection and recognition of iris images

Iris images acquired from a partially cooperating subject often suffer from blur, occlusion due to eyelids, and specular reflections. The performance of existing iris recognition systems degrade significantly on these images. Hence it is essential to select good images from the incoming iris video stream, before they are input to the recognition algorithm. In this paper, we propose a sparsity based algorithm for selection of good iris images and their subsequent recognition. Unlike most existing algorithms for iris image selection, our method can handle segmentation errors and a wider range of acquisition artifacts common in iris image capture. We perform selection and recognition in a single step which is more efficient than devising separate specialized algorithms for the two. Recognition from partially cooperating users is a significant step towards deploying iris systems in a wide variety of applications.

[1]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[2]  F.W. Wheeler,et al.  Stand-off Iris Recognition System , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.

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

[4]  Patrick J. Flynn,et al.  Image understanding for iris biometrics: A survey , 2008, Comput. Vis. Image Underst..

[5]  Yuanning Liu,et al.  A quality evaluation method of iris images sequence based on wavelet coefficients in "region of interest" , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[6]  Marco Gamassi,et al.  Quality assessment of biometric systems: a comprehensive perspective based on accuracy and performance measurement , 2005, IEEE Transactions on Instrumentation and Measurement.

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

[8]  Dexin Zhang,et al.  Personal Identification Based on , 2003 .

[9]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Donald M. Monro,et al.  Iris image selection and localization based on analysis of specular reflection , 2007 .

[12]  B. Dorizzi,et al.  A new probabilistic Iris Quality Measure for comprehensive noise detection , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[13]  Michael P. Friedlander,et al.  Probing the Pareto Frontier for Basis Pursuit Solutions , 2008, SIAM J. Sci. Comput..

[14]  Luís A. Alexandre,et al.  A Method for the Identification of Noisy Regions in Normalized Iris Images , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[15]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  James R. Matey,et al.  Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments , 2006, Proceedings of the IEEE.

[17]  Zihan Zhou,et al.  Demo: Robust face recognition via sparse representation , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[18]  Luís A. Alexandre,et al.  UBIRIS: A Noisy Iris Image Database , 2005, ICIAP.

[19]  Dia I. Abu-Al-Nadi,et al.  Automated personal identification system based on human iris analysis , 2007, Pattern Analysis and Applications.

[20]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[21]  Kang Ryoung Park,et al.  Fake Iris Detection by Using Purkinje Image , 2006, ICB.

[22]  K.W. Bowyer,et al.  Learning to predict gender from iris images , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[23]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

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