Hand movement recognition for brazilian sign language: a study using distance-based neural networks

Authors:
Daniel B. Dias;Renata C. B. Madeo;Thiago Rocha;Helton H. Bíscaro;Sarajane M. Peres
Affiliations:
School of Arts, Sciences and Humanities, University of São Paulo, Brazil;School of Arts, Sciences and Humanities, University of São Paulo, Brazil;School of Arts, Sciences and Humanities, University of São Paulo, Brazil;School of Arts, Sciences and Humanities, University of São Paulo, Brazil;School of Arts, Sciences and Humanities, University of São Paulo, Brazil
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 7
Cited 6

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Self-Organizing Maps

Self-Organizing Maps
A Real-Time Continuous Gesture Recognition System for Sign Language

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Real-time American Sign Language recognition from video using hidden Markov models

ISCV '95 Proceedings of the International Symposium on Computer Vision
Real time gesture recognition using continuous time recurrent neural networks

Proceedings of the ICST 2nd international conference on Body area networks
Gesture Recognition: A Survey

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A dynamic gesture recognition system for the Korean sign language (KSL)

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

A committee machine implementing the pattern recognition module for fingerspelling applications

Proceedings of the 2010 ACM Symposium on Applied Computing
Gesture recognition for fingerspelling applications: an approach based on sign language cheremes

Proceedings of the 12th international ACM SIGACCESS conference on Computers and accessibility
Brazilian sign language multimedia hangman game: a prototype of an educational and inclusive application

The proceedings of the 13th international ACM SIGACCESS conference on Computers and accessibility
Reservoir-based evolving spiking neural network for spatio-temporal pattern recognition

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Sign language recognition with support vector machines and hidden conditional random fields: going from fingerspelling to natural articulated words

MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Rule-based trajectory segmentation for modeling hand motion trajectory

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the vision-based hand movement recognition problem is formulated for the universe of discourse of the Brazilian Sign Language. In order to analyze this specific domain we have used the artificial neural networks models based on distance, including neural-fuzzy models. The experiments explored here show the usefulness of these models to extract helpful knowledge about the classes of movements and to support the project of adaptative recognizer modules for Libras-oriented computational tools. Using artificial neural networks architectures - Self Organizing Maps and (Fuzzy) Learning Vector Quantization, it was possible to understand the data space and to build models able to recognize hand movements performed for one or more than one specific Libras users.