Region-based SIFT approach to iris recognition

Traditional iris recognition systems transfer iris images to polar (or log-polar) coordinates and have performed very well on data that tends to have a centered gaze. The patterns of an iris are part of a 3-D structure that is captured as a two-dimensional (2-D) image and cooperative iris recognition systems are capable of correctly matching these 2-D representations of iris features. However, when the gaze of an eye changes with respect to the camera lens, many times the size, shape, and detail of iris patterns will change as well and cannot be matched to enrolled images using traditional methods. Additionally, the transformation of off-angle eyes to polar coordinates becomes much more challenging and noncooperative iris algorithms will require a different approach. The direct application of the scale-invariant feature transform (SIFT) method would not work well for iris recognition because it does not take advantage of the characteristics of iris patterns. We propose the region-based SIFT approach to iris recognition. This new method does not require polar transformation, affine transformation or highly accurate segmentation to perform iris recognition and is scale invariant. This method was tested on the iris challenge evaluation (ICE), WVU and IUPUI noncooperative databases and results show that the method is capable of cooperative and noncooperative iris recognition.

[1]  Dexin Zhang,et al.  Efficient iris recognition by characterizing key local variations , 2004, IEEE Transactions on Image Processing.

[2]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[3]  Yingzi Eliza Du.  Review of iris recognition: cameras, systems, and their applications , 2006 .

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

[5]  Chein-I Chang,et al.  3D combinational curves for accuracy and performance analysis of positive biometrics identification , 2008 .

[6]  Thad Welch,et al.  Use of one-dimensional iris signatures to rank iris pattern similarities , 2006 .

[7]  Tieniu Tan,et al.  Improving iris recognition accuracy via cascaded classifiers , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[8]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[9]  Gerhardt Muller.  COMMUNICATIONS AND INFORMATION TECHNOLOGIES , 1995 .

[10]  Renbiao Wu,et al.  Iris Recognition Based on Local Feature Point Matching , 2006, 2006 International Symposium on Communications and Information Technologies.

[11]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[12]  Matthew A. Brown,et al.  Invariant Features from Interest Point Groups , 2002, BMVC.

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

[14]  Anil K. Jain,et al.  Localized Iris Image Quality Using 2-D Wavelets , 2006, ICB.

[15]  Yingzi Du,et al.  A Selective Feature Information Approach for Iris Image-Quality Measure , 2008, IEEE Transactions on Information Forensics and Security.

[16]  Charles V. Stewart,et al.  Keypoint Descriptors for Matching Across Multiple Image Modalities and Non-linear Intensity Variations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Linda G. Shapiro,et al.  A SIFT descriptor with global context , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[18]  Yingzi Du,et al.  Performance analysis and parameter optimization for iris recognition using Log-Gabor wavelet , 2007, Electronic Imaging.

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

[20]  John Daugman.  How iris recognition works , 2004 .

[21]  B. V. K. Vijaya Kumar,et al.  A Bayesian Approach to Deformed Pattern Matching of Iris Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[23]  Chein-I. Chang,et al.  New Hyperspectral Discrimination Measure for Spectral Characterization , 2004 .