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Minutia tensor matrix: a new strategy for fingerprint matching
1Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, China.
Plos One
|March 31, 2015
Summary
This study introduces a novel tensor matching strategy for fingerprint recognition using minutia tensor matrices (MTMs). This approach enhances accuracy and robustness by analyzing both local and global fingerprint features.
Area of Science:
- Biometrics
- Computer Science
- Pattern Recognition
Background:
- Establishing correspondences between minutia sets is crucial for fingerprint recognition.
- Existing methods face challenges with local rigidity and global nonlinearity in fingerprint images.
Purpose of the Study:
- To propose a new tensor matching strategy for robust fingerprint recognition.
- To enhance the description ability and robustness of minutia matching algorithms.
Main Methods:
- Introduced the concept of minutia tensor matrix (MTM) to describe first and second-order features of minutia pairs.
- Developed local and global MTMs to leverage local rigidity and global compatibility.
- Proposed a two-level matching algorithm utilizing both local and global MTMs.
Main Results:
- The MTM captures similarities and compatibilities, formulating matching as dense sub-block recovery.
- The two-level algorithm effectively utilizes local and global fingerprint characteristics.
- Experiments on FVC2002 and FVC2004 databases demonstrated improved effectiveness and efficiency.
Conclusions:
- The proposed tensor matching strategy offers stronger descriptive ability and better robustness to noise and nonlinearity.
- The method shows significant promise for advancing fingerprint recognition technology.
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