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

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 dat...

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Vydáno v:Neural computing & applications Ročník 33; číslo 16; s. 10449 - 10467
Hlavní autoři: Rezende, Tamires Martins, Almeida, Sílvia Grasiella Moreira, Guimarães, Frederico Gadelha
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.08.2021
Springer Nature B.V
Témata:
Artificial Intelligence
Artificial neural networks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer vision
Data Mining and Knowledge Discovery
Data structures
Deep learning
Gesture recognition
Image Processing and Computer Vision
Machine learning
Original Article
Pattern recognition
Probability and Statistics in Computer Science
Recording
Sign language
Sign language recognition
Deep learning
Database
MINDS-Libras
Brazilian sign language
CNN
ISSN:0941-0643, 1433-3058
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Popis
Shrnutí: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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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-05802-4