Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44061
Title: Feature and computational time reduction on hand biometric system
Authors: Travieso, Carlos M. 
Solé-Casals, Jordi
Ferrer, Miguel A. 
Alonso, Jesús B. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Shape
Principal Component Analysis
Pattern Recognition
Hand Biometric System
Parameterization
Feature Reduction
Classification System
Issue Date: 2010
Conference: 3rd International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 2010) 
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.
URI: http://hdl.handle.net/10553/44061
ISBN: 978-989-674-018-4
Source: BIOSIGNALS 2010 - Proceedings of the 3rd International Conference on Bio-inpsired Systems and Signal Processing, Proceedings, p. 367-372
Appears in Collections:Actas de congresos
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