Abstract
Recently, biometrics technology has been receiving attention as means of personal authentication in smartphone environment. Fingerprint recognition is generally contained in newest smartphones and other biometric methods such as iris recognition are receiving attention. However, these methods have a problem of being not applicable to existing smartphones because additional devices such as infrared cameras or sensors should be included. To solve this problem, in the present paper, a new biometric method using features on the rear of the thumb is proposed. The similarity between enrolled thumb images and input thumb images is measured through the SIFT (Scale Invariant Feature Transform) method. Through feasibility tests, it could be identified that the proposed method could recognize the thumb with an accuracy level of approximately 99.94%.
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References
Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14, 20–44 (2004)
Jain, A.K., Nandakumar, K., Nagr, A.: Biometric template security. EURASIP J. Adv. Sig. Process. 2008, 1–17 (2008)
Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14, 21–30 (2004)
Lee, E.C., Lee, H.C., Park, K.R.: Thumb vein recognition using minutia based alignment and local binary pattern based feature extraction. Int. J. Imaging Syst. Technol. 19, 179–186 (2009)
Lee, S.H., Lee, D.W.: FinTech-conversions of finance industry based on ICT. Korea Convergence Soc. 6, 97–102 (2015)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miler, E.: Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Technical report 1, 07–49, University of Massachusetts, Amherst (2007)
Wang, L., Leedham, G., Cho, D.S.Y.: Minutiae feature analysis for infrared hand vein pattern biometrics. Pattern Recogn. 41, 920–929 (2008)
Tan, X., Triggs, W.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19, 1635–1650 (2010)
Bicego, M., Lagorio, A., Grosoo, E., Tistarelli, M.: On the use of SIFT features for face authentication. 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2006). IEEE (2006)
Acknowledgements
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (grant number NRF-2016R1C1B2014345). Also, this research was supported by the Ministry of Science, ICT and Future Planning (MSIP), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2016-H8501-16-1014) supervised by the Institute for Information & Communications Technology Promotion (IITP).
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Lim, N., Ko, D., Suh, K.H., Lee, E.C. (2017). Thumb Biometric Using Scale Invariant Feature Transform. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_15
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DOI: https://doi.org/10.1007/978-981-10-5041-1_15
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