Authors:
Carlos M. Travieso
1
;
Jordi Solé-Casals
2
;
Miguel A. Ferrer
1
and
Jesús B. Alonso
1
Affiliations:
1
Technological Centre for Innovation in Communnications, University of Las Palmas de Gran Canaria, Spain
;
2
Digital Technologies Group, University of Vic, Spain
Keyword(s):
Principal Component Analysis, Pattern Recognition, Hand Biometric System, Parameterization, Feature reduction, Classification system.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Multimedia
;
Multimedia Signal Processing
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Telecommunications
Abstract:
In real-time biometric systems, computational time is a critical and important parameter. In order to improve it, simpler systems are necessary but without loosing classification rates. In this present work, we explore how to improve the characteristics of a hand biometric system by reducing the computational time. For this task, neural network-multi layer Perceptron (NN-MLP) are used instead of original Hidden Markov Model (HMM) system and classical Principal Component Analysis (PCA) procedure is combined with MLP in order to obtain better results. As showed in the experiments, the new proposed PCA+MLP system achieves same success rate while computational time is reduced from 247 seconds (HMM case) to 7.3 seconds.