A new similarity digest search strategy applied to Minutia Cylinder-Codes for fingerprint identification
Resumo
One challenging problem on the fingerprint realm is the identification of individuals over large databases, where the most similar template must be found. Approximate matching is used in digital forensic investigations to deal efficiently with large amount of data. We think it can also be used to identify similar fingerprints with compact representations and become a promising technique to speed up searches. In this paper, we explore this hypothesis and present MCC-HBFT, a new fingerprint identification strategy based on the approximate matching technique HBFT and the state-of-the-art fingerprint representation model MCC. We show how MCC-HBFT identify fingerprints and outperforms a commonly used indexing strategy in some public databases of the field.
Referências
Breitinger, F., Baier, H., and White, D. (2014a). On the database lookup problem of approximate matching. Digital Investigation, 11:S1–S9.
Breitinger, F., Guttman, B., McCarrin, M., Roussev, V., and White, D. (2014b). Approximate matching: definition and terminology. NIST Special Publication, 800:168.
Breitinger, F., Rathgeb, C., and Baier, H. (2014c). An efficient similarity digests database lookup-a logarithmic divide & conquer approach. JDFSL, 9(2):155.
Cappelli, R., Ferrara, M., and Maltoni, D. (2010). Minutia cylinder-code: A new representation and matching technique for fingerprint recognition. IEEE TPAMI, 32(12):2128–2141.
Cappelli, R., Ferrara, M., and Maltoni, D. (2011). Fingerprint indexing based on minutia cylinder-code. IEEE TPAMI, 33(5):1051–1057.
FVC2002 (2018). The second fingerprint verification competition. [link]. Accessed 2018 Jun 20.
FVC2004 (2018). The third international fingerprint verification competition. [link]. Accessed 2018 Jun 20.
ISO/IEC-19794-2:2005 (2005). Information technology - biometric data interchange formats - part 2: Finger minutiae data.
Kayaoglu, M., Topcu, B., and Uludag, U. (2013). Standard fingerprint databases: Manual minutiae labeling and matcher performance analyses. arXiv preprint arXiv:1305.1443.
Ko, K. (2007). User’s guide to nist biometric image software (nbis). Technical report.
Lillis, D., Breitinger, F., and Scanlon, M. (2017). Expediting mrsh-v2 approximate matching with hierarchical bloom filter trees. In ICDF2C, pages 144–157. Springer.
Moia, V. H. G. and Henriques, M. A. A. (2018). MCC-HBFT: A fingerprint identification strategy. [link]. Accessed 2018 Jun 30.
NIST (2018). Special database 4. [link]. Accessed 2018 Jun 20.
Parmar, P. A. and Degadwala, S. D. (2015). Fingerprint indexing approaches for biometric database: A review. IJCA, 130(13):0975–8887.
Peralta, D., Galar, M., Triguero, I., Paternain, D., García, S., Barrenechea, E., Benítez, J. M., Bustince, H., and Herrera, F. (2015). A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation. Information Sciences, 315:67–87.
Soni, U. A. and Goyani, M. M. (2018). A survey on state of the art methods of fingerprint recognition. IJSRSET, 4.
Su, Y., Feng, J., and Zhou, J. (2016). Fingerprint indexing with pose constraint. Pattern Recognition, 54:1–13.
Wang, Y., Wang, L., Cheung, Y.-m., and Yuen, P. C. (2014). Fingerprint geometric hashing based on binary minutiae cylinder codes. In ICPR, pages 690–695. IEEE.
Wang, Y., Wang, L., Cheung, Y.-M., and Yuen, P. C. (2015). Learning compact binary codes for hash-based fingerprint indexing. IEEE TIFS, 10(8):1603–1616.