FingRF: A Generalized Fingerprints Research Framework
Biometrics is an emerging technology for consistent automatic identification and authentication applications. Fingerprint is the dominant trait between different biometrics like iris, retina, and face. Many fingerprint-based algorithms have been individually developed to investigate, build, or enhance different AFIS components such as fingerprint acquisition, pre-processing, features extraction, and matching. The common shortage of these contributions is the missing of complete platform to ensemble all system components to study the impact of developing one component on the others. This paper introduces FingRF as ongoing fingerprint research framework that links all fingerprint system components with some other supporting tools for performance evaluation. FingRF aims to provide a facility for conducting fingerprint research in a reliable environment. Moreover, it can be extended to include both off-line and on-line operational modes. The prototype version of FingRF is targeted to work as a stable research environment, and hence, it may be extended further for other biometrics technologies.
KeywordsBiometrics Fingerprints Performance Evaluation Matlab®
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- 2.Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of fingerprint recognition. Springer (2009)Google Scholar
- 3.Khan, M.A.U., Khan, M.K., Khan, M.A.: Fingerprint image enhancement using decimation-free directional filter bank. Asian Network for Scientific Information, Information Technology Journal 4, 16–20 (2005)Google Scholar
- 6.Awad, A.I., Abd Allah, M.M., Ali, M.M.: Fingerprint image enhancement algorithm based on wavelet filters. In: Proceedings of the Al Azhar Engineering Ninth International Conference, pp. 100–113. Al Azhar University, Cairo (2007)Google Scholar
- 7.Klimanee, C., Nguyen, D.T.: Classification of fingerprints using singular points and their principal axes. In: Proceedings of the IEEE International Conference on Image Processing (ICIP 2004), pp. 849–852. IEEE, Singapore (2004)Google Scholar
- 9.Wang, W., Li, J., Chen, W.: Fingerprint classification using improved directional field and fuzzy wavelet neural networks. In: Proceedings of the IEEE Sixth World Congress on Intelligent Control and Automation, pp. 9961–9964. IEEE, Dalian (2006)Google Scholar
- 11.Awad, A.I., Baba, K.: Toward an efficient fingerprint classification. In: Albert, M. (ed.) Biometrics - Unique and Diverse Applications in Nature, Science, and Technology. InTech (2011)Google Scholar
- 12.Watson, C.I., Wilson, C.L.: NIST special database 4, fingerprint database (1992)Google Scholar
- 15.SecuGen: Biometric Solution, http://www.secugen.com/products/sensor_usb.html
- 16.Jain, A.K., Bolle, R., Pankanti, S.: Biometrics personal identification in networked society. Springer (2005)Google Scholar