A novel approach for fingerprint classification system based on new feature area search

• Autorzy: Abd Allah M. M.
• Języki publikacji: EN

Abstrakty

EN

The paper presents a new fast fingerprint classification method based on direction patterns. The method is designed to be applicable to today's embedded systems for fingerprint authentication, in which small area sensors are employed (large enough to capture all the core and delta points of a fingerprint). The proposed procedure consists of four steps. First, ridge direction is determined at the pixel level. Second, average orientation field flow is assessed within 8x8 blocks. Then pattern matching is applied to determine presence of either of three "feature areas". Finally, the target classes are identified through a novel classification approach, called generally a pattern area. We prove that the search of direction pattern in a specific area is able to classify fingerprints clearly and quickly. With our algorithm, the classification accuracy of 94% is achieved over 4000 images in the NIST-4 database, slightly lower than the conventional approaches. However, the classification speed has improved tremendously, up to about 10 times faster than the conventional singular point approaches at the pixel level.

Słowa kluczowe

EN

fingerprint classification; direction pattern; embedded system

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2005
• Tom: Vol. 14, No. 2
• Strony: 201--212
• Opis fizyczny: Bibliogr. 11 poz., il.

Twórcy

autor

Abd Allah M. M.

• Minia University, Faculty of Engineering, Department of Electrical Communications and Electronics Section,

Bibliografia

• [1] Henry E.R.: Classification and Use of Fingerprint, Routledge, London, 1990.
• [2] Moayer B., Fu K.S.: A syntactic approach to fingerprint recognition. Pattern Recognition, 1-23, Jul., 1975.
• [3] Kameshwar Rao C.V., Black K.: Type classification of fingerprints: a syntactic approach. IEEE Trans. PAMI, 2(3), 223-231, 1980.
• [4] Candela G.T., Chellappa R.: Comparative Performance of Classification Method for Fingerprints. NIST Technical Report, NISTIR. 5136, Apr., 1993.
• [5] Watson C.I.: Nist Special Database 14, Fingerprint Database. U.S. National Institute of Standards and Technology, 1993.
• [6] Wilson C.L., Candela G.T., Waston C.I.: Neural network fingerprint classification. Artifical Neural Networks, 1(2), 203-228, 1993.
• [7] Candela G.T . et al.: PCASYS-A Pattern-Level Classification Automatic System for Fingerprints. NIST Technical Report, NISTIR 547, Aug., 1995.
• [8] Fitz A.P., Green R.J.: Fingerprint classification using hexagonal fast Fourier transform. Pattern Recognition, 29(10), 1587-1597, 1996.
• [9] Cappelli R., Lumini A., Maio D., Maltoni D.: Fingerprint classification by directional image partitioning. IEEE Trans. on PAMI, 21(5), 402-421, 1996.
• [10] Jain A. K., Prabhaker S., Hong L.: A multichannel approach to fingerprint classification. IEEE Trans. PAMI, 21(4), 348-359, 1996.
• [11] Jain A. K., Parikanti S.: Automated fingerprint identification and imaging systems. IEICE Trans. On Information and System, vol.E84-D, 7, 788-799, 2001.