Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.

[1]  Jianzhong Wang,et al.  An adaptively weighted sub-pattern locality preserving projection for face recognition , 2010, J. Netw. Comput. Appl..

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

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

[4]  Hugo Proença,et al.  Iris Recognition: An Analysis of the Aliasing Problem in the Iris Normalization Stage , 2006, 2006 International Conference on Computational Intelligence and Security.

[5]  Stephanie Schuckers,et al.  A novel biorthogonal wavelet network system for off-angle iris recognition , 2010, Pattern Recognit..

[6]  Tieniu Tan,et al.  Ordinal Measures for Iris Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Yosi Keller,et al.  Scale-Invariant Features for 3-D Mesh Models , 2012, IEEE Transactions on Image Processing.

[8]  Tieniu Tan,et al.  Toward Accurate and Fast Iris Segmentation for Iris Biometrics , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Dong Hui Xu,et al.  Automated classification of protein crystallization images using support vector machines with scale-invariant texture and Gabor features. , 2006, Acta crystallographica. Section D, Biological crystallography.

[10]  Yingzi Du,et al.  Region-based SIFT approach to iris recognition , 2009 .

[11]  Richard P. Wildes,et al.  A machine-vision system for iris recognition , 2005, Machine Vision and Applications.

[12]  Phalguni Gupta,et al.  Robust iris indexing scheme using geometric hashing of SIFT keypoints , 2010, J. Netw. Comput. Appl..

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

[14]  John Daugman,et al.  Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns , 2001, International Journal of Computer Vision.

[15]  Ying Chen.  An Effective Iris Recognition System Based on Combined Feature Extraction and Enhanced Support Vector Machine Classifier , 2013 .

[16]  Jing Huang,et al.  Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet , 2010, Neurocomputing.

[17]  Ching Y. Suen,et al.  Iris recognition using shape-guided approach and game theory , 2011, Pattern Analysis and Applications.

[18]  Tieniu Tan,et al.  Affine invariant classification and retrieval of texture images , 2003, Pattern Recognit..

[19]  Richard P. Wildes,et al.  Iris recognition: an emerging biometric technology , 1997, Proc. IEEE.

[20]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[21]  David G. Lowe,et al.  Local feature view clustering for 3D object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[22]  Gregory C Sharp,et al.  Scale invariant feature transform in adaptive radiation therapy: a tool for deformable image registration assessment and re-planning indication , 2013, Physics in medicine and biology.

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

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

[25]  Qiuqi Ruan,et al.  Shift and gray scale invariant features for palmprint identification using complex directional wavelet and local binary pattern , 2011, Neurocomputing.

[26]  Tieniu Tan,et al.  Perturbation-enhanced feature correlation filter for robust iris recognition , 2012, IET Biom..

[27]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[28]  Ahmed Bouridane,et al.  Does independent component analysis perform well for iris recognition? , 2012, Intell. Data Anal..

[29]  Phalguni Gupta,et al.  A rotation and scale invariant technique for ear detection in 3D , 2012, Pattern Recognit. Lett..

[30]  Chin-Wang Tao,et al.  Iris Recognition Using Possibilistic Fuzzy Matching on Local Features , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  Pascal Frossard,et al.  Scale-Invariant Features and Polar Descriptors in Omnidirectional Imaging , 2012, IEEE Transactions on Image Processing.

[32]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[33]  Hasan Demirel,et al.  Localized discriminative scale invariant feature transform based facial expression recognition , 2012, Comput. Electr. Eng..

[34]  Javier Ortega-Garcia,et al.  Iris recognition based on SIFT features , 2009, 2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS).

[35]  Chin-Wang Tao,et al.  Iris recognition based on relative variation analysis with feature selection , 2008 .

[36]  Yang Huachao,et al.  Robust and Precise Registration of Oblique Images Based on Scale-Invariant Feature Transformation Algorithm , 2012, IEEE Geoscience and Remote Sensing Letters.

[37]  Prabir Bhattacharya,et al.  Optimal Features Subset Selection and Classification for Iris Recognition , 2008, EURASIP J. Image Video Process..

[38]  Urbano Nunes,et al.  Shift and Rotation Invariant Iris Feature Extraction based on Non-subsampled Contourlet Transform and GLCM , 2012, ICPRAM.

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

[40]  K.W. Bowyer,et al.  The Best Bits in an Iris Code , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[43]  Prabir Bhattacharya,et al.  Iris Recognition with Support Vector Machines , 2006, ICB.

[44]  Javier Ortega-Garcia,et al.  Iris recognition based on SIFT features , 2004, 2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS).

[45]  Gang Wang,et al.  A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method , 2011, Knowl. Based Syst..

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

[47]  Mohammad Reza Daliri,et al.  Automated Diagnosis of Alzheimer Disease using the Scale-Invariant Feature Transforms in Magnetic Resonance Images , 2012, Journal of Medical Systems.

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