Fusion of multiple texture representations for palmprint recognition using neural networks

Authors:
Galal M. BinMakhashen;El-Sayed M. El-Alfy
Affiliations:
College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia;College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Year:
2012

Citing 8
Cited 0

Online Palmprint Identification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Multibiometrics (International Series on Biometrics)

Handbook of Multibiometrics (International Series on Biometrics)
Palmprint identification using feature-level fusion

Pattern Recognition
A survey of palmprint recognition

Pattern Recognition
Personal authentication using multiple palmprint representation

Pattern Recognition
Personal verification using palmprint and hand geometry biometric

AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Data Mining: Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques
Fusion of the textural feature and palm-lines for palmprint authentication

ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

During the last decade, palmprint recognition has received an increasing attention due to the abundant features that can be extracted from the captured palmprint image. However, a single palmprint texture representation may not be sufficient for reliable recognition. Therefore, in this paper we propose a computational model for palmprint recognition/identification by fusing different categories of feature-level representations using a multilayer perceptron (MLP) neural network. Features are extracted using Gabor filters and Principle Component Analysis (PCA) is used to reduce the dimensionality of the feature space by selecting the most relevant features for recognition. The proposed model has shown promising results in comparison with naïve Bayes, rule based and K* algorithm.