The week's pick
Reseach Article
Fingerprint Recognition System Based on Mapping Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 5 - Number 2 |
| Year of Publication: 2010 |
| Authors: Dr. R.K.Shukla, Dr. P.K.Singh, Dayashankar Singh |
10.5120/894-1268
|
Dr. R.K.Shukla, Dr. P.K.Singh, Dayashankar Singh . Fingerprint Recognition System Based on Mapping Approach. International Journal of Computer Applications. 5, 2 ( August 2010), 1-5. DOI=10.5120/894-1268
Abstract
Fingerprint identification system is mainly consisted of fingerprint achieving, fingerprint classification and fingerprint matching. Fingerprint matching is the key to the system and effects on the precision and efficiency of the whole system directly. Fingerprints are matched mainly based on their fingerprint texture pattern which can be described with the orientation field of fingerprints. A fingerprint, which has the different orientation angle structure in different local area of the fingerprint and has a texture pattern correlation among the neighboring local areas of the fingerprint, can be viewed as a Markov stochastic field. A novel method of fingerprint matching, which is based on embedded Hidden Markov Model (HMM) that is used for modeling the fingerprint’s orientation field, is described in this paper. The accurate and robust fingerprint matching can be achieved by matching embedded Hidden Markov Model parameters which were built after the processing of extracting features from a fingerprint, forming the samples of observation vectors and training the embedded Hidden Markov Model parameters.
References
- N. Ratha, K. Karu, S. Chen, A. K. Jain, “A real-time matching system for large fingerprint databases”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8, pp. 799-813, 1996.
- Salil Prabhakar, “Fingerprint classification and matching using a filterbank”, Michigan State University, Michigan, USA, 2001.
- Andrew Senior, “A Combination Fingerprint Classifier” IEEE Transactions on pattern analysis and machine intelligence Vol. 10, No. 23, pp. 1165-1174, 2001.
- Andrew Senior, “A Hidden Markov Model Fingerprint Classifier”, In proceedings of the 31st Asilomar conference on Signals, Systems and Computers, pp. 306-310, 1997.
- Nefian A V, HayesIII M H, “Face recognition using an Embedded-HMM”, Proceedings of IEEE Internationa Conference on Audio and Video-based Biometric Person Authentication, Washington D.C.USA, pp. 19-21, 1999.
- Rabiner L, “A tutorial on HMM and selected applications in speech recognition”, Proc. IEEE, Vol. 77, No. 2, pp. 257-286 1989.
- Guo Hao ， Ou ZongYing, He Yang, “Automatic fingerprint classification based on embedded Hidden Markov Models” International Conference on Machine Learning and Cybernetics Vol. 5, pp. 3033-3038, 2003.
- Ou Zongying, Guo Hao, Wei Honglei, “Fingerprint classifie using embedded Hidden Markov Models”, Sinobiometrics‟04, pp 517-522, 2004.
- Chul-Hyun Park, Sang-Keun Oh, Dong-Min Kwak, Bum-Soo Kim, Young-Chul Song, Kil-Houm Park, “A New Referenc Point Detection Algorithm Based on Orientation Pattern Labeling in Fingerprint Images”, IBPRIA, pp. 697-703, 2003.
- D. Maltoni, D. Maio, Anil K. Jain, Salil Prabhakar, “HandbookofFingerprintRecognition”, Springer-Verlag, New York, USA, 2003.
Index Terms
Keywords