Nederlands
Contact
Sitemap
Search
OrganisationEEMCS EPrints Service
 |
|
|
||||||||||||||||||||||||||||||||||
|
|
|
|
|
||||||||||||||||||||||||||||||||
|
|
|
||||||||||||||||||||||||||||||||||
|
|
|
|
|
||||||||||||||||||||||||||||||||
|
|
Klein, S. and Bazen, A.M. and Veldhuis, R.N.J.
(2002)
Fingerprint segmentation based on hidden Markov models.
In: 13th Annual Workshop on Circuits Systems and Signal Processing (ProRISC), Veldhoven, The Netherlands.
pp. 310-318.
Technology Foundation STW.
ISBN 90-73461-33-2
Full text available as:
AbstractAn important step in fingerprint recognition is segmentation. During segmentation the fingerprint image is decomposed into foreground, background and low-quality regions. The foreground is used in the recognition process, the background is ignored. The low-quality regions may or may not be used, dependent on the recognition method. Pixel features of the gray-scale image form the basis of segmentation [3]. The feature vector of each pixel is classified, the class determining the region. Most of the known methods result in a fragmented segmentation, which is removed by means of post-processing. We solve the problem of fragmented segmentation by using a hidden Markov model (HMM) for the classification. The pixel features are modelled as the output of a hidden Markov process. The HMM makes sure that the classification is consistent with the neighbourhood. The performance of HMM-based segmentation highly depends on the choice of pixel features. This paper describes the systematic evaluation of a number of pixel features. HMM-based segmentation turns out to be less fragmented than direct classification. Quantitative measures also indicate improvement.
Export this item as: To correct this item please ask your editor Repository Staff Only: edit this item |
|
|
||||||||||||||||||||||||||||||||
|
|
|
||||||||||||||||||||||||||||||||||
|
|
|
|
|
||||||||||||||||||||||||||||||||
|
|
|
||||||||||||||||||||||||||||||||||
