Uncovering vein pattern using generative adversarial network

Vein distribution is important in medical treatments. It could also be used for identity authentication1 . As a basic part of our body, the blood vessel has the merits of universality and distinctiveness. However, vein patterns are usually not visible in color images, which carries significant limitation. To address this limitation, we proposed a deep-learningbased method. Our method can uncover vein distributions from color images, help relieving pains to patients and widening the application scenarios of vein patterns. Experimental results showed that the proposed method has reliable performance and robustness in varying environments.

[1]  Jianping Li,et al.  User identification based on finger-vein patterns for consumer electronics devices , 2010, IEEE Transactions on Consumer Electronics.

[2]  Tomas Pfister,et al.  Learning from Simulated and Unsupervised Images through Adversarial Training , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  P. Kubelka,et al.  New contributions to the optics of intensely light-scattering materials. , 1954, Journal of the Optical Society of America.

[4]  Leon A. Gatys,et al.  Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[6]  Thomas Brox,et al.  Generating Images with Perceptual Similarity Metrics based on Deep Networks , 2016, NIPS.

[7]  Yoshua Bengio,et al.  Generative Adversarial Networks , 2014, ArXiv.

[8]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[9]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[10]  Alexei A. Efros,et al.  Toward Multimodal Image-to-Image Translation , 2017, NIPS.

[11]  Dumitru Erhan,et al.  Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  John E. Hopcroft,et al.  Stacked Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Tie-Yan Liu,et al.  Dual Learning for Machine Translation , 2016, NIPS.

[14]  Adams Wai-Kin Kong,et al.  Uncovering vein patterns from color skin images for forensic analysis , 2011, CVPR 2011.

[15]  Kejun Wang,et al.  A study of hand vein recognition method , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[16]  Ping Tan,et al.  DualGAN: Unsupervised Dual Learning for Image-to-Image Translation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[17]  Raymond Y. K. Lau,et al.  Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[18]  Chuan Li,et al.  Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks , 2016, ECCV.

[19]  Bin Qin,et al.  The Anti-spoofing Study of Vein Identification System , 2009, 2009 International Conference on Computational Intelligence and Security.

[20]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[21]  Alan C. Bovik,et al.  A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.

[22]  Jan Kautz,et al.  Unsupervised Image-to-Image Translation Networks , 2017, NIPS.

[23]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[24]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[25]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[27]  Rob Fergus,et al.  Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.

[28]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Lior Wolf,et al.  One-Sided Unsupervised Domain Mapping , 2017, NIPS.

[30]  Ajay Kumar,et al.  Human Identification Using Palm-Vein Images , 2011, IEEE Transactions on Information Forensics and Security.

[31]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[32]  Abhinav Gupta,et al.  Generative Image Modeling Using Style and Structure Adversarial Networks , 2016, ECCV.

[33]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.