Unconstrained Iris Segmentation using Convolutional Neural Networks

The extraction of consistent and identifiable features from an image of the human iris is known as iris recognition. Identifying which pixels belong to the iris, known as segmentation, is the first stage of iris recognition. Errors in segmentation propagate to later stages. Current segmentation approaches are tuned to specific environments.

[1]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Andreas Uhl,et al.  Iris Segmentation Using Fully Convolutional Encoder--Decoder Networks , 2017 .

[3]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[4]  Fernando Alonso-Fernandez,et al.  Iris boundaries segmentation using the generalized structure tensor. A study on the effects of image degradation , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[5]  Patrick J. Flynn,et al.  The ND-IRIS-0405 Iris Image Dataset , 2016, ArXiv.

[6]  Hugo Proença,et al.  Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Chun-Wei Tan,et al.  Unified Framework for Automated Iris Segmentation Using Distantly Acquired Face Images , 2012, IEEE Transactions on Image Processing.

[8]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Andreas Uhl,et al.  A Ground Truth for Iris Segmentation , 2014, 2014 22nd International Conference on Pattern Recognition.

[10]  H. Proenca,et al.  The NICE.I: Noisy Iris Challenge Evaluation - Part I , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[11]  Ajay Kumar,et al.  An Accurate Iris Segmentation Framework Under Relaxed Imaging Constraints Using Total Variation Model , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[12]  Peter Corcoran,et al.  An End to End Deep Neural Network for Iris Segmentation in Unconstraint Scenarios , 2017, Neural Networks.

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

[14]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

[17]  Kang Ryoung Park,et al.  A new iris segmentation method for non-ideal iris images , 2010, Image Vis. Comput..

[18]  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.

[19]  Jonathon A. Chambers,et al.  Robust Iris Segmentation Method Based on a New Active Contour Force With a Noncircular Normalization , 2017, IEEE Trans. Syst. Man Cybern. Syst..

[20]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[21]  Andreas Uhl,et al.  Domain Adaptation for CNN Based Iris Segmentation , 2017, 2017 International Conference of the Biometrics Special Interest Group (BIOSIG).

[22]  Josef Kittler,et al.  A survey of the hough transform , 1988, Comput. Vis. Graph. Image Process..

[23]  Zhenan Sun,et al.  Accurate iris segmentation in non-cooperative environments using fully convolutional networks , 2016, 2016 International Conference on Biometrics (ICB).

[24]  Chun-Wei Tan,et al.  Towards Online Iris and Periocular Recognition Under Relaxed Imaging Constraints , 2013, IEEE Transactions on Image Processing.

[25]  Kang Ryoung Park,et al.  IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors , 2018, Sensors.

[26]  Fernando Alonso-Fernandez,et al.  Quality factors affecting iris segmentation and matching , 2013, 2013 International Conference on Biometrics (ICB).

[27]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[30]  Vladimir Vezhnevets,et al.  “GrowCut” - Interactive Multi-Label N-D Image Segmentation By Cellular Automata , 2005 .

[31]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[32]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[33]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[34]  Shahrel Azmin Suandi,et al.  Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut , 2017, Digit. Signal Process..

[35]  Kang Ryoung Park,et al.  Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment , 2017, Symmetry.