On supporting identification in a hand-based biometric framework

Authors:
Pei-Fang Guo;Prabir Bhattacharya;Nawwaf Kharma
Affiliations:
Electrical & Computer Engineering, Concordia University, Montreal, QC, Canada;Computer Science Department, University of Cincinnati, Cincinnati, OH;Electrical & Computer Engineering, Concordia University, Montreal, QC, Canada
Venue:
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Year:
2010

Citing 9
Cited 0

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Genetic programming II: automatic discovery of reusable programs

Genetic programming II: automatic discovery of reusable programs
Machine Learning

Machine Learning
Efficient automatic engineering design synthesis via evolutionary exploration

Efficient automatic engineering design synthesis via evolutionary exploration
Online Palmprint Identification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Application of Projective Invariants in Hand Geometry Biometrics

IEEE Transactions on Information Forensics and Security
Evolving pattern recognition systems

IEEE Transactions on Evolutionary Computation
Knowledge discovery in medical and biological datasets using a hybrid Bayes classifier/evolutionary algorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Research on hand features has drawn considerable attention to the biometric-based identification field in past decades. In this paper, the technique of the feature generation is carried out by integrating genetic programming and the expectation maximization algorithm with the fitness of the mean square error measure (GP-EM-MSE) in order to improve the overall performance of a hand-based biometric system. The GP program trees of the approach are utilized to find optimal generated feature representations in a nonlinear fashion; derived from EM, the learning task results in the simple k-means problem that reveals better convergence properties. As a subsequent refinement of the identification, GP-EM-MSE exhibits an improved capability which achieves a recognition rate of 96% accuracy by using the generated features, better than the performance obtained by the selected primitive features.