Iris biometrics using deep convolutional networks

Iris biometrics-based recognition and verification systems, is an important and well-studied problem. However, recent advances in deep convolution networks make them a viable tool for extracting meaningful attributes and the process of using such an algorithmically generated feature set to classify images may be applied to the iris recognition method. This paper explores the applicability of the Convolutional Neural Network to iris biometrics. It explores the benefits of automatically-generated features compared to the traditional method of hand-crafted features and uses algorithms based on fine tuned models of deep residual networks, to solve both the recognition and the verification problems, resulting in a 99.8% recognition rate.

[1]  Babak Nadjar Araabi,et al.  Pigment Melanin: Pattern for Iris Recognition , 2009, IEEE Transactions on Instrumentation and Measurement.

[2]  Arun Ross,et al.  Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms , 2013, Comput. Vis. Image Underst..

[3]  Reuben A. Farrugia,et al.  Exploring deep learning image super-resolution for iris recognition , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

[4]  Tieniu Tan,et al.  Biometric personal identification based on iris patterns , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[5]  Arun Ross,et al.  Iris Image Reconstruction from Binary Templates , 2016 .

[6]  Shervin Minaee,et al.  An experimental study of deep convolutional features for iris recognition , 2016, 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[7]  Chun-Wei Tan,et al.  Human identification from at-a-distance face images using sparse representation of local iris features , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[8]  Abhishek Kumar Gangwar,et al.  DeepIrisNet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[9]  Raymond N. J. Veldhuis,et al.  Biometric Authentication System on Mobile Personal Devices , 2010, IEEE Transactions on Instrumentation and Measurement.

[10]  Ajay Kumar,et al.  Towards More Accurate Iris Recognition Using Deeply Learned Spatially Corresponding Features , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[11]  Jaime S. Cardoso,et al.  Multi-source deep transfer learning for cross-sensor biometrics , 2016, Neural Computing and Applications.

[12]  Luís A. Alexandre,et al.  The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Shervin Minaee,et al.  Iris recognition using scattering transform and textural features , 2015, 2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE).

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Ajay Kumar,et al.  Comparison and combination of iris matchers for reliable personal authentication , 2010, Pattern Recognit..

[16]  John D Simon,et al.  Metal-ion interactions and the structural organization of Sepia eumelanin. , 2005, Pigment cell research.

[17]  Gian Luca Foresti,et al.  Biometric Liveness Detection: Challenges and Research Opportunities , 2015, IEEE Security & Privacy.

[18]  Rama Chellappa,et al.  Secure and Robust Iris Recognition Using Random Projections and Sparse Representations , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Anirban Mukherjee,et al.  Stroke Point Warping-Based Reference Selection and Verification of Online Signature , 2018, IEEE Transactions on Instrumentation and Measurement.

[20]  Kiran B. Raja,et al.  ContlensNet: Robust Iris Contact Lens Detection Using Deep Convolutional Neural Networks , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

[22]  Paul Meredith,et al.  The physical and chemical properties of eumelanin. , 2006, Pigment cell research.

[23]  R. M. Farouk.  Iris recognition based on elastic graph matching and Gabor wavelets , 2011, Comput. Vis. Image Underst..