%0 Conference Paper %1 rezende2017anlise %A Rezende, Tamires Martins %A Castro, Cristiano Leite de %A Almeida, Sílvia Grasiella Moreira %A Guimarães, Frederico Gadelha %B Anais do XIII Simpósio Brasileiro de Automação Inteligente %D 2017 %I Universidade Federal do Rio Grande do Sul (UFRGS) %K brazilian-sign-language lbp libras local-binary-pattern pattern-recognition real support-vector-machine svm %P 465-470 %T Análise da expressão facial em reconhecimento de sinais de Libras %U https://www.ufrgs.br/sbai17/papers/paper_152.pdf %X In this paper, we propose a methodology to support Brazilian Sign Language (BSL) recognition based on facial expression. We selected 10 signs of BSL - to calm down, to accuse, to annihilate, to love, to gain weight, happiness, slim, lucky, surprise, and angry - whose facial expression changes along its execution. These signals compose an experimental database recorded for our purpose and each signal was recorded 10 times. In this methodology, we execute four steps: (i) detection and clipping of the face, which is the region of interest of this work; (ii) summarizing videos with face images to eliminate redundant information; (iii) creation of the feature vector from the texture descriptor application Local Binary Pattern in each of the significant frames;and (iv) classification of the signals by applying the Support Vector Machine. The automatic recognition system achieved an average accuracy of 94.67%, showing the potential of the proposed methodology for scenarios where facial expression is the tool of discrimination.

@inproceedings{rezende2017anlise, abstract = {In this paper, we propose a methodology to support Brazilian Sign Language (BSL) recognition based on facial expression. We selected 10 signs of BSL - to calm down, to accuse, to annihilate, to love, to gain weight, happiness, slim, lucky, surprise, and angry - whose facial expression changes along its execution. These signals compose an experimental database recorded for our purpose and each signal was recorded 10 times. In this methodology, we execute four steps: (i) detection and clipping of the face, which is the region of interest of this work; (ii) summarizing videos with face images to eliminate redundant information; (iii) creation of the feature vector from the texture descriptor application Local Binary Pattern in each of the significant frames;and (iv) classification of the signals by applying the Support Vector Machine. The automatic recognition system achieved an average accuracy of 94.67%, showing the potential of the proposed methodology for scenarios where facial expression is the tool of discrimination.}, added-at = {2019-08-20T05:35:30.000+0200}, author = {Rezende, Tamires Martins and Castro, Cristiano Leite de and Almeida, Sílvia Grasiella Moreira and Guimarães, Frederico Gadelha}, biburl = {https://www.bibsonomy.org/bibtex/212326d8d212db1df65cb6e53497aa14d/jpmor}, booktitle = {Anais do XIII Simpósio Brasileiro de Automação Inteligente}, eventdate = {2017}, eventtitle = {Simpósio Brasileiro de Automação Inteligente (SBAI)}, interhash = {02ded4e42a2a4eecef2254222375ff5a}, intrahash = {12326d8d212db1df65cb6e53497aa14d}, keywords = {brazilian-sign-language lbp libras local-binary-pattern pattern-recognition real support-vector-machine svm}, language = {Portuguese}, pages = {465-470}, publisher = {Universidade Federal do Rio Grande do Sul (UFRGS)}, school = {Universidade Federal de Minas Gerais (UFMG)}, timestamp = {2020-10-07T13:36:50.000+0200}, title = {Análise da expressão facial em reconhecimento de sinais de Libras}, url = {https://www.ufrgs.br/sbai17/papers/paper_152.pdf}, year = 2017 }