Abstract
In this paper a palm vein identification system is presented, which exploits the strength of convolutional neural network (CNN) architectures. We built and compared six different CNN approaches for biometric identification based on palm images. Four of them were developed by applying transfer learning and fine-tuning techniques to relevant deep learning architectures in the literature (AlexNet, VGG-16, ResNet-50 and SqueezeNet). We proposed and analysed two novel CNN architectures as well. We experimentally compared the identification accuracy and training convergence of these models. Each model was trained and evaluated using the PUT palm vein near infrared image database. To increase the accuracy obtained, we investigated the influence of some image quality enhancement methods, such as contrast adjustment and normalization, Gaussian smoothing, contrast limited adaptive histogram equalization, and Hessian matrix based coarse vein segmentation. Results show high recognition accuracy for almost every such CNN-based approach.
The research was partially supported by Sapientia Foundation – Institute for Scientific Research, and Domus Hungarica Research Grant. L. Szilágyi is János Bolyai Fellow of the Hungarian Academy of Sciences.
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References
Kabaciński, R., Kowalski, M.: Human vein pattern segmentation from low quality images - a comparison of methods. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 2. Advances in Intelligent and Soft Computing, vol. 84, pp. 105–112. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16295-4_12
Meraoumia, A., Chitroub, S., Bouridane, A.: Are infrared images reliable for palmprint based personal identification systems? In: 2013 Saudi International Electronics, Communications and Photonics Conference, pp. 1–6 (2013)
Soliman, H., Mohamed, A.S., Atwan, A.: Feature level fusion of palm veins and signature biometrics. Int. J. Video Image Process. Network Secur. 12(1), 28–39 (2012)
Kang, W., Liu, Y., Wu, Q., Yue, X.: Contact-free palm-vein recognition based on local invariant features. PLoS ONE 9(5), 1–12 (2014)
Lefkovits, S., Emerich, S., Szilágyi, L.: Biometric system based on registration of dorsal hand vein configurations. In: Satoh, S. (ed.) PSIVT 2017. LNCS, vol. 10799, pp. 17–29. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92753-4_2
Lee, Y.P.: Palm vein recognition based on a modified \({2D}^2\) LDA. SIViP 9(1), 229–242 (2015)
Fronitasari, D., Gunawan, D.: Palm vein recognition by using modified of local binary pattern (LBP) for extraction feature. In: 15th International Conference on Quality in Research (QiR), International Symposium on Electrical and Computer Engineering, Nusa Dua, Indonesia, pp. 18–22 (2017)
Al-Juboori, A.M., Bu, W., Wu, X., Zhao, Q.: Palm vein verification using multiple features and isometric projection. Int. J. Signal Process. Image Process. Pattern Recogn. 7(1), 33–44 (2014)
Ma, X., Jing, X., Huang, H., Cui, Y., Mu, J.: Palm vein recognition scheme based on an adaptive gabor filter. IET Biometrics 6(5), 325–333 (2017)
Du, D., Lu, L., Fu, R., Yuan, L., Chen, W., Liu, Y.: Palm vein recognition based on end-to-end convolutional neural network. J. South. Med. Univ. 39(2), 207–214 (2019)
Zhong, D., Liu, S., Wang, W., Du, X.: Palm vein recognition with deep hashing network. In: Lai, J.-H., et al. (eds.) PRCV 2018. LNCS, vol. 11256, pp. 38–49. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03398-9_4
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weniberger, K.Q. (eds.) Advances in Neural Information Processing Systems vol. 25, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: alexnet-level accuracy with 50x fewer parameters and \(<\)0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)
Frangi, A.F., Niessen, W.J., Hoogeveen, R.M., van Walsum, T., Viergever, M.A.: Model-based quantitation of 3-d magnetic resonance angiographic images. IEEE Trans. Med. Imaging 18(10), 946–956 (1999)
Lefkovits, S., Lefkovits, L., Szilágyi, L.: CNN approaches for dorsal hand vein based identification. In: 27th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. Computer Science Research Notes (2019, in press)
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Lefkovits, S., Lefkovits, L., Szilágyi, L. (2019). Applications of Different CNN Architectures for Palm Vein Identification. In: Torra, V., Narukawa, Y., Pasi, G., Viviani, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2019. Lecture Notes in Computer Science(), vol 11676. Springer, Cham. https://doi.org/10.1007/978-3-030-26773-5_26
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