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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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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