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Hand Veins Feature Extraction using DT-CNNs

Malki, Suleyman LU and Spaanenburg, Lambert LU (2007) International Symposium on Microtechnologies for the New Millennium, 2007 In Proceedings of SPIE, the International Society for Optical Engineering 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)
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author
organization
publishing date
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
in
Proceedings of SPIE, the International Society for Optical Engineering
volume
6590
publisher
SPIE
conference name
International Symposium on Microtechnologies for the New Millennium, 2007
external identifiers
  • scopus:36249027007
language
English
LU publication?
yes
id
72db779a-b9c4-4a35-a8e0-9e852a87c455 (old id 603751)
date added to LUP
2007-12-10 14:10:56
date last changed
2017-06-11 04:55:23
@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},
  keyword      = {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},
}