Application of convolutional neural networks for fingerprint recognition
(2018) In Master's Theses in Mathematical Sciences FMAM05 20181Mathematics (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)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8949667
- author
- Lam, Tuong LU and Nilsson, Simon LU
- supervisor
- organization
- course
- FMAM05 20181
- year
- 2018
- 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}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Application of convolutional neural networks for fingerprint recognition}}, year = {{2018}}, }