Abstract
Out of the commonly used techniques, fingerprint authentication till date, remains the most reliable. Previously, a plethora of schemes for identification has been employed, however they failed to address a notable challenge- rotational issues, associated with the fingerprint scheme. This leads to incorrect orientation identification, ultimately leading to error in results. Our paper attempts to solve this issue, by proposing a fast pattern matching technique that caters for differences in orientation by firstly, implementing a pre-matching level called the orientation identification stage, and then match the correctly identified oriented fingerprint image to the stored image. To this end, the derived fingerprint image is intercepted with several scan circles to obtain the minutiae information. This information then, is translated into a string, having its staring point as the least lexicographical rotation value. Using approximate string matching techniques, this string information is matched against a database of stored images. The experiment was conducted on solving the rotation stage to prove the efficiency of this method, where the extracting and re-rotation is done in less than a second, with a linear time algorithm, yet practically sub linear in respect to the short extracted binary strings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, B.C., Xie, S.J., Park, D.S.: Finger vein recognition using optimal partitioning uniform rotation invariant LBP descriptor. J. Electr. Comput. Eng. (2016)
Sebastian, S.: Literature survey on automated person identification techniques. Int. J. Comput. Sci. Mob. Comput. 2(5), 232–237 (2013)
Bai, C., Zhao, T., Wang, W., Wu, M.: An efficient indexing scheme based on k-plet representation for fingerprint database. In: Intelligent Computing Theories and Methodologies, pp. 247–257. Springer (2015)
Mngenge, N.A., Mthembu, L., Nelwamondo, F.V., Ngejane, C.H.: An integrated approach to fingerprint indexing using spectral clustering based on minutiae points. In: IEEE Science and Information Conference (SAI), pp. 1222–1229 (2015)
Agarwal, A., Sharma, A.K., Khandelwal, S.: Study of rotation oriented fingerprint authentication. Int. J. Emerg. Eng. Res. Technol. 2(7), 211–214 (2014)
Perez-Diaz, A., Arronte-Lopez, I.: Fingerprint matching and non-matching analysis for different tolerance rotation degrees in commercial matching algorithms. J. Appl. Res. Technol. 8(2), 186–198 (2010)
Mandi, R., Lokhande, S.: Rotation-invariant fingerprint identification system. Int. J. Electron. Commun. Comput. Technol. (IJECCT) 2(4) (2012)
Lakshmanan, R., Selvaperumal, S., Mun, C.: Integrated finger print recognition using image morphology and neural network. Int. J. Adv. Stud. Comput. Sci. Eng. (IJASCE) 3(1), 40–48 (2014)
AL-Jamea, M., Athar, T., Iliopoulos, C.S., Pissis, S.P., Sohel Rahman, M.: A novel pattern matching approach for fingerprint-based authentication. In: PATTERNS 2015, pp. 45–49 (2015)
Chen, W., Sui, L., Xu, Z., Lang, Y.: Improved Zhang-Suen thinning algorithm in binary line drawing applications. In: 2012 International Conference on Systems and Informatics (ICSAI). IEEE, pp. 1947–1950 (2012)
NIST: Biometric special databases and software (2015). http://www.nist.gov. Accessed Jan 2016
Unar, J., Seng, W.C., Abbasi, A.: A review of biometric technology along with trends and prospects. Pattern Recogn. 47(8), 2673–2688 (2014)
OpenCV-code: Implementation of guo-hall thinning algorithm (2015). http://opencv-code.com/quick-tips/implementation-of-guo-hall-thinning-algorithm/. Accessed Jan 2016
Guo, J.-M., Liu, Y.-F., Chang, J.-Y., Lee, J.-D.: Fingerprint classification based on decision tree from singular points and orientation field. Expert Syst. Appl. 41(2), 752–764 (2014)
Zhang, Q., Yan, H.: Fingerprint classification based on extraction and analysis of singularities and pseudo ridges. Pattern Recogn. 37(11), 2233–2243 (2004)
Chen, X., Tian, J., Yang, X.: A new algorithm for distorted fingerprints matching based on normalized fuzzy similarity measure. IEEE Trans. Image Process. 15(3), 767–776 (2006)
Lee, H.C., Ramotowski, R., Gaensslen, R.E. (eds.): Advances in Fingerprint Technology, 2nd edn. CRC Press, Boca Raton (2002)
Henry, E.R.: Classification and uses of finger prints. HM Stationery Office (1905)
Su, Y., Feng, J., Zhou, J.: Fingerprint indexing with pose constraint. Pattern Recogn. 54, 1–13 (2016)
Peralta, D., Galar, M., Triguero, I., Paternain, D., García, S., Barrenechea, E., Benítez, J.M., Bustince, H., Herrera, F.: A survey on fingerprint minutiae-based local matching for verification and identification: taxonomy and experimental evaluation. Inf. Sci. 315, 67–87 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Ajala, O., Aljamea, M., Alzamel, M., Iliopoulos, C.S. (2018). Fast Fingerprint Rotation Recognition Technique Using Circular Strings in Lexicographical Order. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-56991-8_71
Download citation
DOI: https://doi.org/10.1007/978-3-319-56991-8_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56990-1
Online ISBN: 978-3-319-56991-8
eBook Packages: EngineeringEngineering (R0)