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Application of convolutional neural networks for fingerprint recognition

Lam, Tuong LU and Nilsson, Simon LU (2018) In Master's Theses in Mathematical Sciences FMAM05 20181
Mathematics (Faculty of Engineering)
Abstract
Fingerprint recognition is a well-known problem in pattern recognition and
widely used in contemporary authentication technology such as access devices in mobile phones. The subject of this thesis is to investigate the applicability of convolutional neural networks for fingerprint recognition. This is accomplished by designing various network architectures for this task. Our starting-point is an architecture known as a siamese network, from which we build upon by including additional components as well as network architectures based on the siamese architecture. The networks are realized by
implementation. Data for training and evaluating the networks is provided as gray-scale images of fingerprints and we implement a simple algorithm for... (More)
Fingerprint recognition is a well-known problem in pattern recognition and
widely used in contemporary authentication technology such as access devices in mobile phones. The subject of this thesis is to investigate the applicability of convolutional neural networks for fingerprint recognition. This is accomplished by designing various network architectures for this task. Our starting-point is an architecture known as a siamese network, from which we build upon by including additional components as well as network architectures based on the siamese architecture. The networks are realized by
implementation. Data for training and evaluating the networks is provided as gray-scale images of fingerprints and we implement a simple algorithm for generating ground truth labels. To evaluate our work, we measure the performance of all implemented models with common metrics for fingerprint recognition algorithms. Lastly problems with our approach are listed and potential future improvements are given. (Less)
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author
Lam, Tuong LU and Nilsson, Simon LU
supervisor
organization
course
FMAM05 20181
year
type
H2 - Master's Degree (Two Years)
subject
keywords
convolutional neural network, CNN, neural network, fingerprint, matching, siamese, inception, triplet, capsule
publication/series
Master's Theses in Mathematical Sciences
report number
LUFTMA-3354-2018
ISSN
1404-6342
other publication id
2018:E36
language
English
id
8949667
date added to LUP
2018-07-04 14:40:36
date last changed
2018-07-04 14:40:36
@misc{8949667,
  abstract     = {Fingerprint recognition is a well-known problem in pattern recognition and
widely used in contemporary authentication technology such as access devices in mobile phones. The subject of this thesis is to investigate the applicability of convolutional neural networks for fingerprint recognition. This is accomplished by designing various network architectures for this task. Our starting-point is an architecture known as a siamese network, from which we build upon by including additional components as well as network architectures based on the siamese architecture. The networks are realized by
implementation. Data for training and evaluating the networks is provided as gray-scale images of fingerprints and we implement a simple algorithm for generating ground truth labels. To evaluate our work, we measure the performance of all implemented models with common metrics for fingerprint recognition algorithms. Lastly problems with our approach are listed and potential future improvements are given.},
  author       = {Lam, Tuong and Nilsson, Simon},
  issn         = {1404-6342},
  keyword      = {convolutional neural network,CNN,neural network,fingerprint,matching,siamese,inception,triplet,capsule},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Application of convolutional neural networks for fingerprint recognition},
  year         = {2018},
}