IDENTITY VERIFICATION ON PARTIAL FINGERPRINT IMAGES USING GLOBAL FEATURE VECTORS
Although there are several commercial applications available in the market, fingerprint identification and verification is still a challenging task in the field of automated computing. Despite the common usage of fingerprint identification systems, current calculation algorithms need to be more developed, since the variations in scanned image significantly effects the success rate. Thus people are frequently requested to re-scan their finger in order to eliminate false-positive verifications. Unquestionably, scanned fingerprints are subject to several external distortions, as is casual scars, regional bruises and partially-blinded scanners. Hence a portion of the local features can not be identified at all. In this study the efficiency of global-image-feature set is tested on partial fingerprint scans. Findings indicate that the success rate of the global feature comparison approach on partial finger print images is %70 higher than that of local feature comparison.
Jain, Anil, Ruud Bolle, and Sharath Pankanti, eds. Biometrics: personal identification in networked society. Vol. 479. Springer Science & Business Media, 2006
Peralta, Daniel, et al. "Fast fingerprint identification for large databases." Pattern Recognition 47.2 (2014): 588-602.
Chen, Fanglin, Xiaolin Huang, and Jie Zhou. "Hierarchical minutiae matching for fingerprint and palmprint identification." IEEE Transactions on Image Processing 22.12 (2013): 4964-4971
Nídlová, V., and J. Hart. "Reliability of biometric identification using fingerprints under adverse conditions." Agronomy Research 13.3 (2015): 786-791.
Zainal, Nur Izzati, et al. "Design and development of portable classroom attendance system based on Arduino and fingerprint Biometric." Information and Communication Technology for The Muslim World (ICT4M), 2014 The 5th International Conference on. IEEE, 2014.
Kryszczuk K., Drygajlo A.,& Morier P. (2004). Extraction of level2 and level3 features for fragmentary fingerprints. 2nd COST275 Workshop, (December), 83–88.
Jea, Tsai-Yang, and Venugopal Govindaraju. "Minutiae-based partial fingerprint recognition." State University of New York at Buffalo, Buffalo, NY (2005).
Jain, Anil, Arun Ross, and Salil Prabhakar. "Fingerprint matching using minutiae and texture features." Image Processing, 2001. Proceedings. 2001 International Conference on. Vol. 3. IEEE, 2001.
Jain, Anil K., Yi Chen, and Meltem Demirkus. "Pores and ridges: High-resolution fingerprint matching using level 3 features." IEEE Transactions on Pattern Analysis and Machine Intelligence 29.1 (2007): 15-27.
Zhao, Q., Zhang, D., Zhang, L., & Luo, N. (2010). Adaptive fingerprint pore modeling and extraction. Pattern Recognition, 43(8), 2833-2844.
Abhyankar, Aditya, and Stephanie Schuckers. "Towards integrating level-3 Features with perspiration pattern for robust fingerprint recognition." Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE, 2010.
Singh, C. P., Jain, S., & Jain, A. (2014). Literature survey on fingerprint recognition using level 3 feature extraction method. International Journal of engineering and computer science, 3(1).
Subban, R., & Mankame, D. P. (2013). A study of biometric approach using fingerprint recognition. Lecture Notes on Software Engineering, 1(2), 209.
Xiao, Q., & Raafat, H. (1991). Fingerprint image post processing: a combined statistical and structural approach. Pattern Recognition, 24(10), 985-992.
Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE transactions on information theory, 8(2), 179-187.