Hand Veins Feature Extraction using DT-CNNs
(2007) International Symposium on Microtechnologies for the New Millennium, 2007 6590.- Abstract
- As the identification process is based on the unique patterns of the users, biometrics technologies are expected to provide highly secure authentication systems. The existing systems using fingerprints or retina patterns are, however, very vulnerable. One’s fingerprints are accessible as soon as the person touches a surface, while a high resolution camera easily captures the retina pattern. Thus, both patterns can easily be “stolen” and forged. Beside, technical considerations decrease the usability for these methods. Due to the direct contact with the finger, the sensor gets dirty, which decreases the authentication success ratio. Aligning the eye with a camera to capture the retina pattern gives uncomfortable feeling. On the other hand,... (More)
- As the identification process is based on the unique patterns of the users, biometrics technologies are expected to provide highly secure authentication systems. The existing systems using fingerprints or retina patterns are, however, very vulnerable. One’s fingerprints are accessible as soon as the person touches a surface, while a high resolution camera easily captures the retina pattern. Thus, both patterns can easily be “stolen” and forged. Beside, technical considerations decrease the usability for these methods. Due to the direct contact with the finger, the sensor gets dirty, which decreases the authentication success ratio. Aligning the eye with a camera to capture the retina pattern gives uncomfortable feeling. On the other hand, vein patterns of either a palm of the hand or a single finger offer stable, unique and repeatable biometrics features.
A fingerprint-based identification system using Cellular Neural Networks has already been proposed by Gao. His system covers all stages of a typical fingerprint verification procedure from Image Preprocessing to Feature Matching. This paper performs a critical review of the individual algorithmic steps. Notably, the operation of False Feature Elimination is applied only once instead of 3 times. Furthermore, the number of iterations is limited to 1 for all used templates. Hence, the computational need of the feedback contribution is removed. Consequently the computational effort is drastically reduced without a notable chance in quality. This allows a full integration of the detection mechanism. The system is prototyped on a Xilinx Virtex II Pro P30 FPGA. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/603751
- author
- Malki, Suleyman LU and Spaanenburg, Lambert LU
- organization
- publishing date
- 2007
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Hand-Vein Recognition, Image Processing, Field-Programmable Gate-Array, Network on a Chip, Cellular Neural Network
- host publication
- Proceedings of SPIE, the International Society for Optical Engineering
- volume
- 6590
- publisher
- SPIE
- conference name
- International Symposium on Microtechnologies for the New Millennium, 2007
- conference location
- Maspalomas, Gran Canaria, Spain
- conference dates
- 2007-05-02 - 2007-05-04
- external identifiers
-
- scopus:36249027007
- language
- English
- LU publication?
- yes
- id
- 72db779a-b9c4-4a35-a8e0-9e852a87c455 (old id 603751)
- date added to LUP
- 2016-04-04 10:05:26
- date last changed
- 2022-03-15 21:11:58
@inproceedings{72db779a-b9c4-4a35-a8e0-9e852a87c455, abstract = {{As the identification process is based on the unique patterns of the users, biometrics technologies are expected to provide highly secure authentication systems. The existing systems using fingerprints or retina patterns are, however, very vulnerable. One’s fingerprints are accessible as soon as the person touches a surface, while a high resolution camera easily captures the retina pattern. Thus, both patterns can easily be “stolen” and forged. Beside, technical considerations decrease the usability for these methods. Due to the direct contact with the finger, the sensor gets dirty, which decreases the authentication success ratio. Aligning the eye with a camera to capture the retina pattern gives uncomfortable feeling. On the other hand, vein patterns of either a palm of the hand or a single finger offer stable, unique and repeatable biometrics features. <br/><br> A fingerprint-based identification system using Cellular Neural Networks has already been proposed by Gao. His system covers all stages of a typical fingerprint verification procedure from Image Preprocessing to Feature Matching. This paper performs a critical review of the individual algorithmic steps. Notably, the operation of False Feature Elimination is applied only once instead of 3 times. Furthermore, the number of iterations is limited to 1 for all used templates. Hence, the computational need of the feedback contribution is removed. Consequently the computational effort is drastically reduced without a notable chance in quality. This allows a full integration of the detection mechanism. The system is prototyped on a Xilinx Virtex II Pro P30 FPGA.}}, author = {{Malki, Suleyman and Spaanenburg, Lambert}}, booktitle = {{Proceedings of SPIE, the International Society for Optical Engineering}}, keywords = {{Hand-Vein Recognition; Image Processing; Field-Programmable Gate-Array; Network on a Chip; Cellular Neural Network}}, language = {{eng}}, publisher = {{SPIE}}, title = {{Hand Veins Feature Extraction using DT-CNNs}}, volume = {{6590}}, year = {{2007}}, }