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.
Table of Contents
I. INTRODUCTION
II. METHODOLOGY
2.1 Fingerprint matching algorithm
2.2 Input images & Histogram equalization
2.3 Edge detection
2.4 Binarization of the input fingerprints
2.5 Image Segmentation
2.5.1 Block direction estimation
2.6 Thinning of the input fingerprint
2.7 Termination and Bifurcation
2.8 Removal of the false minutia
2.10 ROI Extraction (Morphological Method)
2.11 Minutiae in the ROI of the fingerprints
2.12 Unique minutiae sorter
2.13 Minutiae Matching
2.13.1 Alignment Stage
2.13.2 Matching Stage
III. EXPERIMENTAL RESULTS AND DISCUSSIONS
3.1 The tree diagram of the input fingerprints
3.2 Matrix Equalization
IV. Advantages and Disadvantages
V. CONCLUSIONS
Objectives and Topics
This study aims to develop a robust biometric authentication system by combining minutiae-based feature extraction with a novel "Matrix Equalization" method. The research addresses the challenge of accurately matching fingerprints by implementing a pipeline that includes image enhancement, morphological segmentation, and elastic matching to distinguish between unique fingerprint patterns.
- Minutiae-based fingerprint recognition techniques.
- Image preprocessing and enhancement (binarization, thinning, ROI extraction).
- Development of the Matrix Equalization approach for image comparison.
- Elastic matching algorithms for dealing with fingerprint deformation.
- Performance evaluation and practical applications of fingerprint biometrics.
Excerpt from the book
2.8 Removal of the false minutia
The preprocessing stage does not totally heal the fingerprint image. For example, false ridge breaks due to insufficient amount of ink and ridge cross-connections due to over inking are not totally eliminated. Actually all the earlier stages themselves occasionally introduce some artifacts which later lead to spurious minutia. These false/spurious minutia will significantly affect the accuracy of matching if they are simply regarded as genuine minutia. Different types of false minutia are specified in the following [figure 2.23].
The procedures of removing false minutiae
The equation for the removal of false minutiae is
D = sum all the pixels in the row whose value is one / row length
1. If the distance between one bifurcation and one termination is less than D and the two minutiae are in the same ridge. Remove both of them. Where D is the average interridge width representing the average distance between two parallel neighboring ridges.
2. If the distance between two bifurcations is less than D and they are in the same ridge, remove the two bifurcations.
3. If two terminations are within a distance D and their directions are coincident with a small angle variation. And they suffice the condition that no any other termination is located between the two terminations. Then the two terminations are regarded as false minutia derived from a broken ridge and are removed.
Summary of Chapters
I. INTRODUCTION: Outlines the significance of fingerprints in biometric authentication and the core objective of the research.
II. METHODOLOGY: Details the algorithmic pipeline for fingerprint processing, including enhancement, segmentation, and matching techniques.
III. EXPERIMENTAL RESULTS AND DISCUSSIONS: Presents the practical application of the proposed method using MATLAB and tree diagram analysis.
IV. Advantages and Disadvantages: Evaluates the system's performance metrics and practical limitations in real-world scenarios.
V. CONCLUSIONS: Summarizes the integration of custom morphological operations and the Matrix Equalization method to enhance matching accuracy.
Keywords
Fingerprint, ridge, valley, minutiae extraction, minutiae matching, binarization, smoothing, image segmentation, matrix equalization, bifurcation, biometrics, AFIS, MATLAB, image enhancement, pattern recognition.
Frequently Asked Questions
What is the primary focus of this research paper?
The paper focuses on fingerprint matching using feature extraction techniques and introduces a new method called "Matrix Equalization" to improve the comparison of two fingerprint images.
What are the central themes discussed in the work?
The central themes include biometric authentication, digital image processing (binarization, thinning, contrast enhancement), and the algorithmic alignment and matching of minutiae points.
What is the ultimate goal of the proposed system?
The goal is to accurately distinguish between two fingerprints by developing a reliable minutia extractor and matcher that minimizes error caused by spurious minutiae.
Which scientific methodology is employed?
The methodology combines morphological operations, block direction estimation, and an elastic matching algorithm implemented through MATLAB and Source AFIS software.
What does the main body of the work cover?
It covers the entire pipeline from input image enhancement and ROI extraction to the specific mathematical procedures for identifying and removing false minutiae, and ultimately matching the fingerprints.
Which keywords best characterize this study?
Key terms include minutiae extraction, binarization, matrix equalization, bifurcation, image segmentation, and biometric authentication.
How does the "Matrix Equalization" method work?
It treats fingerprint images as 256x256 matrices in MATLAB and compares them directly; if the matrices are identical, the result is 1, otherwise it is 0.
Why is the "elastic" matching algorithm required?
Because absolute alignment of minutiae points is impossible due to natural skin deformation and variations in data acquisition, an elastic matching approach allows for small discrepancies.
- Citation du texte
- Md. Shahadat Hossain (Auteur), Dr. M. R. Islam (Auteur), 2014, Fingerprint Matching Through Feature Extraction and Matrix Equalization, Munich, GRIN Verlag, https://www.grin.com/document/293865
