Abstract
Touchless fingerprint recognition with high acceptance, high security, hygiene advantages, is currently a hot research field of biometrics, but because of the different image principle of the non-contact fingerprint image and contact fingerprint image, the difference of the two fingerprint image is large. There are still a small number of fuzzy regions in the non-contact fingerprint image after pretreatment, and the traditional method of extracting the future from the detail points can lead to a serious decline in recognition accuracy because of false points. In this paper, the non-contact pretreatment in our laboratory is used according to the characteristics of the contactless fingerprint image, the LBP operator, LGC operator and their improve algorithms are used for image processing; the nearest neighbor classifier is used for feature matching. The experimental result shows that the contactless fingerprint feature extraction method proposed in this paper can obtain higher division fingerprint feature.
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References
Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Technol. 14(1), 4–20 (2004)
Nandini, C., RaviKumar, C.N.: An approach to gait recognition. In: International Symposium on Biometrics and Security Technologies (ISBAST 2008), pp. 1–3 (2008)
Deng, P., Liao, H., Ho, C., Tyan, H.: Wavelet-based off-line handwritten signature verification. Comput. Vis. Image Underst. 76(5), 173–190 (1999)
Hosseinzadeh, D., Krishnan, S.: Gaussian mixture modeling of keystroke patterns for biometric applications. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(6), 816–826 (2008)
Dhruva, N., Rupanagudi, S.R., Sachin, S.K., Sthuthi, B.: Novel segmentation algorithm for hand gesture recognition. In: International Multi-conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4 s), 383–388 (2013)
Li, S., Jain, A.: I NetLibrary. In: Handbook of Face Recognition. Citeseer (2005)
Kafai, M.: Reference face graph for face recognition. IEEE Trans. Inf. Forensics Secur. 9(12), 2132–2143 (2014)
Imtiaz, H., Fattah, S.A.: A wavelet-based dominant feature extraction algorithm for palmprint recognition. Digital Sig. Process. Rev. J. 23(1), 244–258 (2013)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007)
Tong, Y., Chen, R., Cheng, Y.: Facial expression recognition algorithm using LGC based on horizontal and diagonal prior principle. Optik-Int. J. Light Electron Opt. 125(16), 4186–4189 (2014)
Xu, J., Zhang, Y.J.: Expression recognition based on variant sampling method and Gabor features. Comput. Eng. 18, 67 (2011)
Huang, D., Shan, C., Ardabilian, M., et al.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(6), 765–781 (2011)
Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities of China, Natural Science Fund of Heilongjiang Province of China, and Natural Science Foundation of China, under Grand No HEUCF160415, F2015033, and 61573114.
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Wang, K., Jiang, J., Cao, Y., Xing, X., Zhang, R. (2016). Preprocessing Algorithm Research of Touchless Fingerprint Feature Extraction and Matching. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_36
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DOI: https://doi.org/10.1007/978-981-10-3005-5_36
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