Facial action unit detection methodology with application in Brazilian sign language recognition

2021 ◽  
Author(s):  
Emely Pujólli da Silva ◽  
Paula Dornhofer Paro Costa ◽  
Kate Mamhy Oliveira Kumada ◽  
José Mario De Martino
Keyword(s):  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Action Unit ◽  
Facial Action ◽  
Brazilian Sign Language ◽  
Action Unit Detection

scholarly journals Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4359
Author(s):  
José Jair Alves Mendes Junior ◽  
Melissa La Banca Freitas ◽  
Daniel Prado Campos ◽  
Felipe Adalberto Farinelli ◽  
Sergio Luiz Stevan ◽  
...  
Keyword(s):  
Sign Language ◽  
Processing Parameters ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Feature Sets ◽  
Input Signals ◽  
Brazilian Sign Language ◽  
Four Levels ◽  
New Feature ◽  
Overlap Rate

Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.

scholarly journals Facial Expression Analysis in Brazilian Sign Language for Sign Recognition

2018 ◽  
Author(s):  
Rúbia Reis Guerra ◽  
Tamires Martins Rezende ◽  
Frederico Gadelha Guimarães ◽  
Sílvia Grasiella Moreira Almeida
Keyword(s):  
Sign Language ◽  
Support Vector ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
K Nearest Neighbors ◽  
Sign Recognition ◽  
Facial Expression Analysis ◽  
Vector Machines ◽  
Visual Motor ◽  
Brazilian Sign Language

Sign language is one of the main forms of communication used by the deaf community. The language’s smallest unit, a “sign”, comprises a series of intricate manual and facial gestures. As opposed to speech recognition, sign language recognition (SLR) lags behind, presenting a multitude of open challenges because this language is visual-motor. This paper aims to explore two novel approaches in feature extraction of facial expressions in SLR, and to propose the use of Random Forest (RF) in Brazilian SLR as a scalable alternative to Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). Results show that RF’s performance is at least comparable to SVM’s and k-NN’s, and validate non-manual parameter recognition as a consistent step towards SLR.

INDIAN SIGN LANGUAGE RECOGNITION SYSTEM USING OPENPOSE

2019 ◽  
Vol 7 (2) ◽  
pp. 43
Author(s):  
MALHOTRA POOJA ◽  
K. MANIAR CHIRAG ◽  
V. SANKPAL NIKHIL ◽  
R. THAKKAR HARDIK ◽  
◽  
...  
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Indian Sign Language

STATIC DEVNAGARI SIGN LANGUAGE RECOGNITION

2016 ◽  
Vol 3 (3) ◽  
pp. 13
Author(s):  
VERMA VERSHA ◽  
PATIL SANDEEP B. ◽  
◽  
Keyword(s):  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition
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