Abstract
A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.
| Original language | English |
|---|---|
| Title of host publication | 2nd International Conference on Digital Image Processing |
| Volume | 7546 |
| DOIs | |
| Publication status | Published - 2010 Mar 22 |
| Event | 2nd International Conference on Digital Image Processing - Singapore, Singapore Duration: 2010 Feb 26 → 2010 Feb 28 |
Other
| Other | 2nd International Conference on Digital Image Processing |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 10-02-26 → 10-02-28 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
}
Development of neural network techniques for finger-vein pattern classification. / Wu, Jian-Da; Liu, Chiung Tsiung; Tsai, Yi Jang; Liu, Jun Ching; Chang, Ya Wen.
2nd International Conference on Digital Image Processing. Vol. 7546 2010. 75460F.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Development of neural network techniques for finger-vein pattern classification
AU - Wu, Jian-Da
AU - Liu, Chiung Tsiung
AU - Tsai, Yi Jang
AU - Liu, Jun Ching
AU - Chang, Ya Wen
PY - 2010/3/22
Y1 - 2010/3/22
N2 - A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.
AB - A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.
UR - http://www.scopus.com/inward/record.url?scp=77949481663&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77949481663&partnerID=8YFLogxK
U2 - 10.1117/12.852799
DO - 10.1117/12.852799
M3 - Conference contribution
AN - SCOPUS:77949481663
SN - 9780819479426
VL - 7546
BT - 2nd International Conference on Digital Image Processing
ER -