Grammatical facial expression recognition in sign language discourse: a study at the syntax level

Freitas, Fernando; Peres, Sarajane Marques; Lima, Clodoaldo A. M.; Barbosa, Frederico Simões

doi:10.1007/s10796-017-9765-z

Cited by 20 publications

(17 citation statements)

References 38 publications

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“…Most of these studies carry out recognition of the alphabet in their respective languages. Few works focus just on the information of the non-manual expressions [Freitas et al 2017], [Rezende et al 2016], [Uddin 2015], seeking to emphasize the importance of facial expressions in sentences and even the recognition of signs. Although they achieve high rates of recognition of expressions, the common methodologies proposed are adequate only to the set of data used.…”

Section: Related Workmentioning

confidence: 99%

“…Despite recent progress, as seen in [Hinton et al 2012] and [Pigou et al 2015], sign language recognition still lags behind. In particular, Brazilian sign language recognition has only recently been explored ( [Rezende et al 2016], [Freitas et al 2017], [Filho et al 2017]).…”

Section: Introductionmentioning

confidence: 99%

“…Most of the past work in LIBRAS recognition have focused primarily on the manual parameters of signals ([Almeida et al 2014], [Dias et al 2009], [Freitas et al 2017]). This study is an extension of [Rezende et al 2016], which proposes recognition of LI-BRAS signs through facial expression.…”

Section: Introductionmentioning

confidence: 99%

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Facial Expression Analysis in Brazilian Sign Language for Sign Recognition

Guerra¹,

Rezende²,

Guimarães³

et al. 2018

Anais Do XV Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2018)

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Facial Expression Analysis in Brazilian Sign Language for Sign Recognition

Guerra¹,

Rezende²,

Guimarães³

et al. 2018

Anais Do XV Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2018)

View full text Add to dashboard Cite

show abstract

“…Next, the second paper BGrammatical facial expression recognition in sign language discourse: a study at the syntax level,^by Freitas et al (2017) present a study which applies inductive reasoning to recognize patterns, to study the problem involving the automated recognition of Grammatical Facial Expressions (GFEs) at the discourse syntactic level in the Libras (Brazilian) Sign Language. In general, GFEs stand out in automated recognition processes for sign languages, as they help to remove ambiguity among signals, and they also contribute to compose the semantic meaning of discourse.…”

Section: Papers In the Special Issuementioning

confidence: 99%

Big Data Analytics and Business Intelligence in Industry

et al. 2017

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“…A key problem in the study of facial expressions in Libras is the lack of comprehensive annotated corpora to validate the facial expressions shown in the literature. As far as is known, there are only two corpora constructed for the study of facial expressions: Freitas et al (2017) and Rezende et al (2016). However, they are small and have fewer participants involved.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Facial Expressions in Brazilian Sign Language (Libras)

Silva

Kumada

Costa

2021

ESJ

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Brazilian Sign Language (in Portuguese, Libras) is a visuospatial linguistic system adopted by the Brazilian deaf communities as the primary form of communication. Libras are a language of minority groups, thus their research and production of teaching materials do not receive the same incentive to progress or improve as oral languages. This complex language employs signs composed of forms and hands movements combined with facial expressions and postures of the body. Facial expressions rarely appear in sign language literature, despite their being essential to this form of communication. Thereby, this research objectives are to present and discuss sub-categories of the grammatical facial expressions of Libras, with two specific objectives: (1) the building of an annotated video corpus comprehending all the categories identified in the literature of facial expressions in Brazilian sign language; (2) the application of Facial Action Coding System (FACS) (which has its origins as an experimental model in psychology) as a tool for annotating facial expressions in sign language. Ruled by a qualitative approach, the video corpus was carried out with nineteen Libras users (sixteen deaf and three hearing participants) who translated forty- three phrases from Portuguese to Libras. The records were later transcribed with the Eudico Linguistic Annotator software tool. From the analysis of the literature review, it was observed the need to classify facial expression as subcategories of lexical, as intensity, homonyms, and norm. It is believed that it is necessary to expand the studies on facial expressions, favoring their documentation and the description of their linguistic functions. Advances in this sense can contribute to the learning of Libras by deaf students and also by listeners who propose to act as teachers or as translators and interpreters of this language system.

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Grammatical facial expression recognition in sign language discourse: a study at the syntax level

Cited by 20 publications

References 38 publications

Facial Expression Analysis in Brazilian Sign Language for Sign Recognition

Facial Expression Analysis in Brazilian Sign Language for Sign Recognition

Big Data Analytics and Business Intelligence in Industry

Analysis of Facial Expressions in Brazilian Sign Language (Libras)

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