An experimental study of deep convolutional features for iris recognition

Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural networks on general image recognition to other tasks such as segmentation, face recognition, and object detection. In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition. The proposed scheme has been tested on two well-known iris databases, and has shown promising results with the best accuracy rate of 99.4%, which outperforms the previous best result.

[1]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Shervin Minaee,et al.  Palmprint Recognition Using Deep Scattering Convolutional Network , 2016, ArXiv.

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

[5]  Su Yang,et al.  Task sensitivity in EEG biometric recognition , 2018, Pattern Analysis and Applications.

[6]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[7]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[8]  S. Mallat,et al.  Invariant Scattering Convolution Networks , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[10]  Xiaoou Tang,et al.  Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.

[11]  Shervin Minaee,et al.  Fingerprint recognition using translation invariant scattering network , 2015, 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

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

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

[14]  Jason Weston,et al.  Multi-Class Support Vector Machines , 1998 .

[15]  H. Abdi,et al.  Principal component analysis , 2010 .

[16]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[17]  H. Soltanian-Zadeh,et al.  Automatic organs' detection in WCE , 2012, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

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

[19]  References , 1971 .

[20]  Bin Sheng,et al.  Deep Colorization , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[21]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[22]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[23]  Bernhard Schölkopf,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[24]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[26]  Patrick J. Flynn,et al.  A Survey of Iris Biometrics Research: 2008-2010 , 2013, Handbook of Iris Recognition.

[27]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

[29]  Leon A. Gatys,et al.  Texture Synthesis Using Convolutional Neural Networks , 2015, NIPS.

[30]  James A. McHugh,et al.  Automated fingerprint recognition using structural matching , 1990, Pattern Recognit..

[31]  Joseph Picone,et al.  Improved EEG event classification using differential energy , 2015, 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[32]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[33]  H. Soltanian-Zadeh,et al.  Informati ve and Uninformative Regions Detection in WCE Frames , 2014 .

[34]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[35]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[36]  Shervin Minaee,et al.  Highly Accurate Multispectral Palmprint Recognition Using Statistical and Wavelet Features , 2014, ArXiv.

[37]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[39]  Shervin Minaee,et al.  Face recognition using scattering convolutional network , 2016, 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

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

[41]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.