Facial Expression Analysis in Brazilian Sign Language for Sign Recognition

Rúbia Reis Guerra; Tamires Martins Rezende; Frederico Gadelha Guimarães; Sílvia Grasiella Moreira Almeida

doi:10.5753/eniac.2018.4418

Rúbia Reis Guerra UFMG
Tamires Martins Rezende UFMG
Frederico Gadelha Guimarães UFMG
Sílvia Grasiella Moreira Almeida IFMG

DOI: https://doi.org/10.5753/eniac.2018.4418

Resumo

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.

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