Fingerprint Matching Through Feature Extraction and Matrix Equalization

Research Paper (undergraduate) 2014 13 Pages

Computer Science - Applied


Fingerprint Matching Through Feature Extraction and Matrix Equalization

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Abstract: Minutiae based feature extraction methods are used for fingerprint matching. This method is mainly depending on the characteristics of minutiae of the individuals. The minutiae are ridge endings or bifurcations on the fingerprints. Their coordinates and direction are most distinctive features to represent the fingerprint. Most fingerprint matching systems store only the minutiae template in the database for further usage. The conventional methods to utilize minutiae information are treating it as a point set and finding the matched points from different minutiae sets. This kind of minutiae-based fingerprint recognition/matching systems consists of two steps: minutiae extraction and minutiae matching. Image enhancement, histogram equalization, thinning, binarization, smoothing, block direction estimation, image segmantation, ROI extraction etc. are discussed in the minutiae extraction step. After the extraction of minutiae the false minutiae are removed from the extraction to get the accurate result. In the minutiae matching process, the minutiae features of a given fingerprint are compared with the minutiae template and the matched minutiae will be found out. The final template used for fingerprint matching is further utilized in the matching stage to enhance the system’s performance. Two fingerprint images always give two different matrices, the matrix equalization method is also used for matching two fingerprint images after the final template.

Key words: Fingerprint, ridge, valley, minutiae extraction, minutiae matching, binarization, smoothing, image segmentation, matrix equalization and bifurcation.


Fingerprint matching is a widely used biometric authentication system that is done on the basis that every person in the world has its own fingerprint. Every fingerprint has its universal and uniqueness characteristics and widely acceptability. We have mentioned that each fingerprint is formed in the womb during the age 17th week of the fetus and remain unchanged throughout the whole life [13]. In our study we have tried to find out the difference between the input fingerprints by concept of the uniqueness of the fingerprints. For experiment we have taken the concept of matching any two fingerprints from the online journal [8]. We have seen that their input fingerprint, histogram equalization for enhancing the fingerprints, binarization, image segmentation (Block direction estimation and ROI extraction), and false minutia removal, minutia matcher (Alignment stage and matching stage) are discussed. After that we have mentioned an exceptional concept that every image give its own matrix which help to find out the difference between the input fingerprints. Also we have mentioned the tree diagram of the input fingerprint images by which the difference between the two fingerprints can be found out. In the next stage we have seen although each fingerprints have a bifurcation, in the first fingerprint image there are two minutiae in the same direction where in the second fingerprint image the rest two minutiae are in the different direction. Besides feature extraction here we also proposed a new method “Matrix Equalization” in order to find out the difference between the two fingerprint images. In our study we have taken the help of MATLAB coding and AFIS (Automated Fingerprint Identification System) software for implementioning the matching of fingerprints.

Applications of fingerprint matching

- Banking Security - ATM security, card transaction
- Physical Access Control (e.g. Airport)
- Information System Security
- National ID Systems
- Passport control (INSPASS)
- Prisoner, prison visitors, inmate control
- Voter registration
- Identification of Criminals
- Identification of missing children
- Secure E-Commerce (Still under research) [8].


2.1 Fingerprint matching algorithm

The algorithm for matching two fingerprint images are mentioned below

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Figure 2.1: The fingerprint matching procedure

2.2 Input images & Histogram equalization

Histogram equalization is widely used for contrast enhancement in a variety of applications due to its simple function [2].-histeq performs histogram equalization. It enhances the contrast of images by transforming the values in an intensity image so that the histogram of the output image approximately matches a specified histogram (uniform distribution by default) [3].

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Figure 1.2: Image-1 of a fingerprint and its histogram and equalization

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Figure 2.2: Image 2 of another fingerprint and its histogram and equalization

Image enhancement

Sometimes, the images have obtained does not have good quality and thus the purpose of enhancement is to process the image obtained so as to make it clearer by improving perception or interpretability and hence the accuracy of matching will be increased. The quality of image can be upgraded by enhancing the image, and thus the contrast between ridges and valleys can be increased.

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Figure 2.3: The enhanced image-1 after histogram equalization

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Figure 2.4: The enhanced image-2 after histogram equalization

Image enhancement is also done to make the image clearer for making the further operations easier and for increasing the contrast between ridges and furrows and for connecting the false broken points of ridges due to insufficient amount of ink are very useful for keep a higher accuracy to fingerprint matching.

2.3 Edge detectio n

The purpose of edge detection in AFIS is to significantly reduce the amount of data found in a fingerprint image and leave only the most important information. Edge detection works by finding points on an image where the gray scale value changes greatly between pixels [4].

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Figure 2.5: Edge detected of image-1

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Figure 2.6: Edge detected of image-2

2.4 Binarization of the input fingerprints

Binarization is the method in which the 8-bit gray fingerprint images are transformed to a 1-bitimage with 0-value for ridges and 1-value for furrows. After this operation, ridges in the fingerprint are highlighted with black color while furrows are white.

For binarizing assign a pixel as valley (furrow), 255, or ridge 0, from its gray value G(i, j) according to the following rule:

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Figure 2.7: The Binarized images of the fingerprints image-1 and image-2 (From left side)

2.5 Image Segmentation

In general, only a Region of Interest (ROI) is useful to be recognized for each fingerprint image. The image area without effective ridges and furrows is first discarded since it only holds background information. Then the bound of the remaining effective area is sketched out since the minutiae in the bound region are confusing with that spurious minutia that is generated when the ridges are out of the sensor.



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fingerprint matching through feature extraction matrix equalization



Title: Fingerprint Matching Through Feature Extraction and Matrix Equalization