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Computer Aided Technique for Finger Print Image Enhancement and Minutiae Extraction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 75 - Number 17 |
| Year of Publication: 2013 |
| Authors: Rupali Telgad, Prapti Deshmukh |
10.5120/13201-0400
|
Rupali Telgad, Prapti Deshmukh . Computer Aided Technique for Finger Print Image Enhancement and Minutiae Extraction. International Journal of Computer Applications. 75, 17 ( August 2013), 9-15. DOI=10.5120/13201-0400
Abstract
Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, and unacceptable error rates. To represent the large amount of data in the biometric images an efficient feature extraction method is needed. This paper presents the feature extraction of fingerprint image processing stages such as image Preprocessing, Converting the image in to gray scale, Image Enhancement can be performed with the help of Discrete Fourier transformation, image Binarization, image segmentation, image thinning, Minutiae Extraction after Minutiae Extraction again segmentation technique is used . In finger print images there are foreground regions and background regions where foreground region show the ridges and valleys while the background regions are to be left out. . The foreground regions have high value while the background regions have low values. Segmentation separates the foreground region from the background image for the reliable extraction of minutiae. The feature extraction is the first step used for matching used in biometric recognition. After feature extraction the next step is preprocessing of Minutiae. The extracted feature is called as templates, which is used in matching.
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