Total views : 785
Fingerprint Matching through Back Propagation Neural Network
Objectives: This paper is focused with original features used in fingerprint matching founded on back propagation neural network. For finding the features in the images, it can be improved using filtering techniques as isotropic and anisotropic. Isotropic can protect features on the input images but can barely progress the dominance of the images. But on the contrary, anisotropic filtering can successfully eliminate noise from the image just when a consistent point of reference is provided. Methods and Analysis: The filters commonly used namely median filter, gabor filter along with anisotropic filters are used for filtering the noises under direct gray scale enhancement .Whereas for an input image, the narrow ridge direction is predictable and the region of interest is positioned. The uniqueness of a fingerprint is exclusively determined by the local ridge characteristics and their relationships. The ridges and valleys in a fingerprint alternate, flowing in a local constant direction. The two most prominent local ridge characteristics are: 1) ridge ending and, 2) ridge bifurcation. A ridge ending is defined as the point where a ridge ends rapidly. A ridge bifurcation is defined as the point where a ridge forks or diverges into branch ridges. Collectively, these features are called minutiae. Minutiae points are extracted during the enrollment process and then for each authentication. In a fingerprint, they correspond to either a ridge ending or a bifurcation. Minutiae are major features of a fingerprint using which comparisons of one print with another can be made. After finding the minutiae points the filters reducing image noises, smoothing, removing some forms of misfocus and motion blur, is in the front step of image processing. Filtering is also used for preserving the true ridge and valley structures. Findings: The digital results of these features are practically feeded as input of the neural network using median filter, gabor filter, anisotropic filter for training function. For fingerprint identification the confirmation part of the system identifies the fingerprint based training show of the network. Novelty and Improvement: To finish the new outcome reveals that the number of accepted sample rate of the proposed method using the three filters which is far better than the existing fingerprint verification system using artificial neural network
Anisotropic Filter, Artificial Neural Network, Back Propagation Algorithm, Gabor Filter, Median Filter
- Cuntoor N, Chellappa R. A framework for activity-specific human identification. Proc ICASSP; 2003 May.
- Jain AK, Mao J. Artificial neural networks: A tutorial IEEE Computer. 1996 Mar; 29(3):31–44. Crossref
- Veluchamy M, Perumal K, Ponuchamy T. Feature Extraction and Classification of Blood Cells using Artificial Neural Network. American Journal of Applied Sci. 2012; 9:615–9. ISSN: 1546-9239.
- Mohamed S, Nyongesa H. Automatic fingerprint classification system using fuzzy neural techniques. IEEE International Conference on Fuzzy Systems. IEEE Xplore; Washington; 2002; 1:358–62. Crossref
- Anil J, Sharath P. Automated Fingerprint Identification and Imaging systems. Technical Report 500-89. National Bureau of Standards; 1988.
- Hong IJ, Pankati AKS, Bolle SR. Fingerprint enhancement. In: Proc First IEEE WACV; Sarasota. p. 202–7
- Gornale SS, Humbe V, Manza R, Kale KV. Fingerprint image de-noising using Multi-Resolution Analysis (MRA)through Stationary Wavelet Transform (SWT) method. International Journal of Knowledge Engineering. 2010; 1(1):5–14. ISSN: 0976–5816.
- Yang GZ, Burger P, Firmin DN, Underwood SR. Structure adaptive anisotropic filtering Image and Vision Computing. 1996; 14:135–45.
- Afsar FA, Arif M, Hussain M. Fingerprint Identification and Verification System using Minutiae Matching.
- National Conference on Emerging Technologies; 2004. PMCid:PMC3548212
- Chaur CC, Yaw-YW. An AFIS Using Fingerprint Classification; North Palmerston; 2003 Nov.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.