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Development and validation of a Brazilian sign language database for human gesture recognition

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Abstract

Sign language recognition is considered the most important and challenging application in gesture recognition, involving the fields of pattern recognition, machine learning and computer vision. This is mainly due to the complex visual–gestural nature of sign languages and the availability of few databases and studies related to automatic recognition. This work presents the development and validation of a Brazilian sign language (Libras) public database. The recording protocol describes (1) the chosen signs, (2) the signaller characteristics, (3) the sensors and software used for video acquisition, (4) the recording scenario and (5) the data structure. Provided that these steps are well defined, a database with more than 1000 videos of 20 Libras signs recorded by twelve different people is created using an RGB-D sensor and an RGB camera. Each sign was recorded five times by each signaller. This corresponds to a database with 1200 samples of the following data: (1) RGB video frames, (2) depth, (3) body points and (4) face information. Some approaches using deep learning-based models were applied to classify these signs based on 3D and 2D convolutional neural networks. The best result shows an average accuracy of 93.3%. This paper presents an important contribution for the research community by providing a publicly available sign language dataset and baseline results for comparison.

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Notes

  1. See https://stats.stackexchange.com/questions/211436.

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Acknowledgements

The authors would like to thank Marcos Antônio Alves and Aline Xavier Fidêncio for the textual revision, and everyone who participated in the construction of the MINDS-Libras, especially all the people who volunteered their time to execute the signs. This work was partially financed by the Foundation for Research of the State of Minas Gerais [Fundação de Amparo à Pesquisa do Estado de Minas Gerais—FAPEMIG (Grant No. PPM-00587-16)], by the Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES), which is a Brazilian federal government agency under the Ministry of Education, by Federal Intitute of Minas Gerais (Instituto Federal de Minas Gerais—IFMG), and by the National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq), Brazil, Grants Nos. 306850/2016-8, 167016/2017-2 and 312991/2020-7, and Notice No. 169/2015.

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  1. Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, MG, 31270-901, Brazil

    Tamires Martins Rezende

  2. Department of Industrial Automation, Federal Institute of Minas Gerais - Ouro Preto, Pandiá Calógeras Street, 898, Bauxita, Ouro Preto, MG, 35400-000, Brazil

    Sílvia Grasiella Moreira Almeida

  3. Machine Intelligence and Data Science (Minds) Laboratory, School of Engineering, Federal University of Minas Gerais, Av. Antônio Carlos 6627, Block I Room 2200, Belo Horizonte, MG, 31270-901, Brazil

    Frederico Gadelha Guimarães

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Rezende, T.M., Almeida, S.G.M. & Guimarães, F.G. Development and validation of a Brazilian sign language database for human gesture recognition. Neural Comput & Applic 33, 10449–10467 (2021). https://doi.org/10.1007/s00521-021-05802-4

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