A novel reference point detection method is proposed by exploiting the GPM(Gradient Probabilistic Model) that captures the curvature information of fingerprint texture. The detection of reference point is accomplished through searching and locating the points of occurrence of the most evenly distributed gradient in probabilistic sense. We also propose a novel filterbank method to improve shortcoming of existing filterbank method in verification part. Existing filterbank method can lose the discerning attributes because the sectors of the outer band from the reference point are larger in size than those of the inner bands. Such shortcomings of the filterbank method are resolved by maintaining the attribute regions to equal size.
|Number of pages||11|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2003 Dec 1|
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Computer Science(all)
- Theoretical Computer Science