The week's pick
Reseach Article
Fingerprint Compression using Sparse Representation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 3 |
| Year of Publication: 2017 |
| Authors: Priya Bharti |
10.5120/ijca2017915908
|
Priya Bharti . Fingerprint Compression using Sparse Representation. International Journal of Computer Applications. 179, 3 ( Dec 2017), 32-36. DOI=10.5120/ijca2017915908
Abstract
Biometric identification systems are in use for last many years for the purpose of personal identification, uncompressed graphics, audio and video data require considerable storage capacity and transmission bandwidth dealing with such enormous amount of information can often present difficulties. As per my literature survey, there is no such method that uses compressive sensing and adaptive learning dictionary to compress image along with neural network to estimate the results. In the given algorithm, a dictionary of predefined fingerprint patches is constructed which is than quantized and encoded.
References
- Zhang Qinghui and Zhang Xiangfei, “Research of Key Algorithm in the Technology of Fingerprint Identification,” Second IEEE International Conference on Computer Modeling and Simulation, pp. 282-284, 2010.
- A. J. Ferreira and M. A. T. Figueiredo, “On the use of independent component analysis for image compression,” Signal Process., Image Commun., vol. 21, no. 5, pp. 378–389, 2006.
- P. Paatero and U. Tapper, “Positive matrix factorization: A nonnegative factor model with optimal utilization of error estimates of data values,” Environmetrics, vol. 5, no. 1, pp. 111–126, 1994.\
- D. D. Leeand and H. S. Seung, “Learning the parts of objects by nonnegative matrix factorization,” Nature, vol. 401, pp. 799–791, Oct. 1999.
- Criminal Justice Information Services Division, ‘Gray-Scale Fingerprint Image Compression Specification’, Federal Bureau of Investigation,2010
- Ms.Mansi Kamblin , ‘Fingerprint Image Compression’, International Journal of Engineering Science and Technology Vol. 2(5),2010
- V. Conti, C.Militello, F.Sorbello,et.al. “Introducing Pseudo Singularity Points for Efficient Fingerprints Classification and Recognition,” IEEE International Conference on Complex, Intelligent and Software Intensive Systems, pp. 368-375,2010.
Index Terms
Keywords