EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 128 (2017) MATEC Web Conf., 128 (2017) 04015 References
Open Access
Issue
MATEC Web Conf.
Volume 128, 2017
2017 International Conference on Electronic Information Technology and Computer Engineering (EITCE 2017)
Article Number 04015
Number of page(s) 5
Section Computer Programming
DOI https://doi.org/10.1051/matecconf/201712804015
Published online 25 October 2017
  1. Vacca J.R. Biometric Technologies and Verification Systems[M]. Butterworth-Heinemann, 2007. [Google Scholar]
  2. Kulkarni S, Raut D R. Finger Vein Recognition[J]. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 2014: 32–36. [Google Scholar]
  3. Bichler O. CONVOLUTIONAL NEURAL NETWORK:, WO/2016/030230[P]. 2016. [Google Scholar]
  4. Yang J, Li X. Efficient Finger Vein Localization and RecognitionfC]// International Conference on Pattern Recognition. IEEE, 2010:1148–1151. [Google Scholar]
  5. Guan F, Wang K, Yang Q. A study of two direction weighted (2D)2LDA for finger vein recognition[C]// International Congress on Image and Signal Processing. IEEE, 2011: 860–864. [Google Scholar]
  6. Yang W, Rao Q, Liao Q. Personal Identification for Single Sample Using Finger Vein Location and Direction Coding[C]// International Conference on Hand-Based Biometrics. IEEE, 2011:1–6. [Google Scholar]
  7. Gupta P, Gupta P. An accurate finger vein based verification system[J]. Digital Signal Processing, 2015, 38:43–52. [CrossRef] [EDP Sciences] [Google Scholar]
  8. Wu J D, Liu C T. Finger-vein pattern identification using principal component analysis and the neural network technique[J]. Expert Systems with Applications, 2011, 38(5):5423–5427. [CrossRef] [Google Scholar]
  9. Wang K Q, Khisa A S, Wu X.Q, et al. Finger vein recognition using LBP variance with global matching[C]// International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2012:196–201. [Google Scholar]
  10. Hubel D H, Weisel T N. Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in cat’s visual cortex. J. Physiol. (London) 160, 106–154[J]. 1962, 160. [Google Scholar]
  11. Breu H, Gil J, Kirkpatrick D, et al. Linear time Euclidean distance transform algorithms[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 17(5):529–533. [CrossRef] [Google Scholar]
  12. Yuan Z W, Zhang J. Feature extraction and image retrieval based on AlexNet[C]// Eighth International Conference on Digital Image Processing. 2016:100330E. [Google Scholar]
  13. Liu W, Wen Y, Yu Z, et al. Large-Margin Softmax Loss for Convolutional Neural Networks[J]. 2016. [Google Scholar]
  14. http://www.datatang.com [Google Scholar]
  15. Jia, Yangqing, Shelhamer, et al. Caffe: Convolutional Architecture for Fast Feature Embedding[J]. 2014:675–678. [EDP Sciences] [Google Scholar]
  16. Neyshabur B, Salakhutdinov R, Srebro N. Path-SGD: Path-Normalized Optimization in Deep Neural Networks[J]. Computer Science, 2015. [Google Scholar]
  17. Liu Z et al. Finger vein recognition with manifold learning. Journal of Network and Computer Applications. 2010; 33(3):275–82. [CrossRef] [Google Scholar]
  18. Perez Vega A, Travieso CM, Alonso JB. Biometric personal identi cation system based on patterns created by nger veins. 2014 International Work Conference on Bio-inspired Intelligence (IWOBI), IEEE. 2014. [Google Scholar]
  19. Dong S, Yang J, Chen Y, et al. Finger Vein Recognition Based on Multi-Orientation Weighted Symmetric Local Graph Structure[J]. Ksii Transactions on Internet & Information Systems, 2015, 9(10):4126–4142. [Google Scholar]
  20. Xie S J, Lu Y, Yoon S, et al. Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex.[J]. Sensors, 2015, 15(7):17089–17105. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.