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1:N fingerprint classification

Xiang, Linda LU and Petersson, Ellen LU (2018) In Master's Theses in Mathematical Sciences FMAM05 20181
Mathematics (Faculty of Engineering)
Abstract
Biometric recognition systems are widely used to recognize an individual. Fingerprints is a biometric identifier and are today widely used in smartphones for biometric recognition. The fingerprint software used in smartphones are great and fast, and usually implemented for one person usage. A fingerprint software used for smartphones often conducts a one-to-one comparison between the sample and the enrolled templates. Software systems that can be used to recognize a person out of many are desirable. Such a system would conduct one-to-many comparisons. However, using the existing software in smartphones in a product used by many people would be too slow since it has to conduct many one-to-one comparisons between the sample and the enrolled... (More)
Biometric recognition systems are widely used to recognize an individual. Fingerprints is a biometric identifier and are today widely used in smartphones for biometric recognition. The fingerprint software used in smartphones are great and fast, and usually implemented for one person usage. A fingerprint software used for smartphones often conducts a one-to-one comparison between the sample and the enrolled templates. Software systems that can be used to recognize a person out of many are desirable. Such a system would conduct one-to-many comparisons. However, using the existing software in smartphones in a product used by many people would be too slow since it has to conduct many one-to-one comparisons between the sample and the enrolled templates.

This thesis examines whether it is possible to classify the enrolled templates by K-centroid, such that the number of comparisons in the one-to-many authentication problem is reduced. It was shown that the bag-of-words representation of an image is the best feature to use, together with the cosine similarity, during classification. To further improve the clustering, SVM and balancing were also applied. This combination showed good results and is the most promising method out of all methods examined in this thesis. (Less)
Please use this url to cite or link to this publication:
author
Xiang, Linda LU and Petersson, Ellen LU
supervisor
organization
course
FMAM05 20181
year
type
H2 - Master's Degree (Two Years)
subject
keywords
K-centroid, similarity measure, SVM, templates, Bag-of-words
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3357-2018
ISSN
1404-6342
other publication id
2018:E39
language
English
id
8954340
date added to LUP
2018-07-04 14:42:08
date last changed
2018-07-04 14:42:08
@misc{8954340,
  abstract     = {Biometric recognition systems are widely used to recognize an individual. Fingerprints is a biometric identifier and are today widely used in smartphones for biometric recognition. The fingerprint software used in smartphones are great and fast, and usually implemented for one person usage. A fingerprint software used for smartphones often conducts a one-to-one comparison between the sample and the enrolled templates. Software systems that can be used to recognize a person out of many are desirable. Such a system would conduct one-to-many comparisons. However, using the existing software in smartphones in a product used by many people would be too slow since it has to conduct many one-to-one comparisons between the sample and the enrolled templates. 

This thesis examines whether it is possible to classify the enrolled templates by K-centroid, such that the number of comparisons in the one-to-many authentication problem is reduced. It was shown that the bag-of-words representation of an image is the best feature to use, together with the cosine similarity, during classification. To further improve the clustering, SVM and balancing were also applied. This combination showed good results and is the most promising method out of all methods examined in this thesis.},
  author       = {Xiang, Linda and Petersson, Ellen},
  issn         = {1404-6342},
  keyword      = {K-centroid,similarity measure,SVM,templates,Bag-of-words},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {1:N fingerprint classification},
  year         = {2018},
}