Abstract
To overcome the shortcomings of the traditional methods, in this paper, we investigate the role of a biologically-inspired network for finger-vein recognition. Firstly, robust feature representation of finger-vein images are obtained from an enhanced Hierarchical and X (HMAX) model, and successively class by the extreme learning machine (ELM). The enhanced HMAX model could calculate complex feature representations by the way of simulating the hierarchical processing mechanism in primate visual cortex. ELM performs well in classification while keeping a faster learning speed. Our proposed method is tested on the MMCBNU-6000 dataset, and achieved good performances compared with state-of-the-art methods. The results further the case for biologically-motivated approaches for finger-vein recognition.
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Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics, 1st edn. Springer, Berlin (2008)
Kono, M., Ueki, H. and Umemura, S.: A new method for the identification of individuals by using of vein pattern matching of a finger. In: Proceedings of Fifth Symposium on Pattern Measurement, pp. 9–12 (2000)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. J. Pattern Recogn. 29, 51–59 (1996)
Rosdi, B.A., Shing, C.W., Suandi, S.A.: Finger vein recognition using local line binary pattern. J. Sens. 11, 11357–11371 (2011)
Miura, N., Nagasaka, A., Miyatake, T.: Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach. Vis. Appl. 15, 194–203 (2004)
Xie, S.J., Yang, J.C., Yoon, S., Lu, Y., Park, D.S.: Guided Gabor filter for finger vein pattern extraction. In: Proceedings of the 8th International Conference on Signal Image Technology and Internet Based Systems, pp. 118–123 (2012)
Huang, G.B., Zhu, Q.Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 Proceedings of IEEE International Joint Conference on Neural Networks, IEEE 2004, pp. 985–990 (2004)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. J. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42, 513–529 (2012)
Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE CVPR 2005, pp. 994–1000 (2005)
Kumar, A., Zhou, Y.: Human identification using finger images. IEEE Trans. Image Process. 21, 2228–2244 (2012)
Zhang, Y., Zhang, L., Li, P.: A novel biologically inspired ELM-based network for image recognition. J. Neurocomput. 174, 286–298 (2016)
Liu, B.C., Xie, S.J., Park, D.S.: Finger vein recognition using optimal partitioning uniform rotation invariant LBP descriptor. In: Proceedings of the 6th International Congress on Image and Signal Processing (CISP 2015), pp. 410–415 (2015)
Dong, S., Yang, J.C., Park, D.S.: Finger vein recognition based on multi-orientation weighted symmetric local graph structure. J. KSII Trans. Internet Inf. Syst. 9, 4126–4142 (2015)
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant No. 61502338 and No. 61502339, the 2015 key projects of Tianjin science and technology support program No. 15ZCZDGX00200, and the Open Fund of Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis No. GDUPTKLAB201504.
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Sun, W., Yang, J., Xie, Y., Fang, S., Liu, N. (2016). Finger-Vein Recognition Based on an Enhanced HMAX Model. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_29
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DOI: https://doi.org/10.1007/978-3-319-46654-5_29
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