Facial Expressions Recognition in Sign Language Based on a Two-Stream Swin Transformer Model Integrating RGB and Texture Map Images

Authors

  • Lourdes Ramírez Cerna Universidad Nacional de Trujillo
  • José Antonio Rodriguez Melquiades Universidad Nacional de Trujillo
  • Edwin Jonathan Escobedo Cárdenas Universidad de Lima
  • Guillermo Cámara Chávez Universidade Federal de Ouro Preto
  • Dayse Garcia Miranda Universidade Federal de Ouro Preto

DOI:

https://doi.org/10.13053/cys-29-2-5119

Keywords:

Facial expressions in sign language, RGBD data, texture map images, two-stream architecture, Swin Transformer.

Abstract

The study of facial expressions in sign language has become a significant research area, as these expressions not only convey personal states, but also enhance the meaning of signs within specific contexts. The absence of facial expressions during communication can lead to misinterpretations, underscoring the need for datasets that include facial expressions in sign language. To address this, we present the Facial-BSL dataset, which consists of videos capturing eight distinct facial expressions used in Brazilian Sign Language. Additionally, we propose a two-stream model designed to classify facial expressions in a sign language context. This model utilizes RGB images to capture local facial information and texture map images to record facial movements. We assessed the performance of several deep learning architectures within this two-stream framework, including Convolutional Neural Networks (CNNs) and Vision Transformers. In addition, experiments were conducted using public datasets such as CK+, KDEF-dyn, and LIBRAS. The two-stream architecture based on the Swin Transformer model demonstrated superior performance on the KDEF-dyn and LIBRAS datasets and achieved a second-place ranking on the CK+ dataset, with an accuracy of 97% and an F1-score of 95%.

Author Biographies

Lourdes Ramírez Cerna, Universidad Nacional de Trujillo

She received a Bachelor’s degree in Computer Science from the National University of Trujillo, Peru, in 2011, and a Master’s degree in Computer Science from the Federal University of Ouro Preto, Brazil, in 2015. She currently holds the position of Professor at the University of Lima. Her areas of interest include computer vision and machine learning.

José Antonio Rodriguez Melquiades, Universidad Nacional de Trujillo

He holds a Bachelor’s degree in Mathematics from the National University of Trujillo, Peru; a Master’s degree in Computer Science from the Federal University of Minas Gerais, Brazil; and a Ph.D. in Transportation from the University of Brasilia in Brazil. He is currently a Full Professor at the National University of Trujillo. His areas of interest include Combinatorial Optimization and Logistics

Edwin Jonathan Escobedo Cárdenas, Universidad de Lima

He received a Bachelor’s degree in Computer Science from the National University of Trujillo, Peru, in 2011, followed by a Master’s and Ph.D. in Computer Science from the Federal University of Ouro Preto, Brazil, in 2013-2019. He currently holds the position of professor at the University of Lima. His research interests include Computer Vision, Machine Learning, and Deep Learning.

Guillermo Cámara Chávez, Universidade Federal de Ouro Preto

He received a Bachelor’s degree in Systems Engineering from the Santa Maria Catholic University, Peru, in 1996, and a Master’s degree in Computer Science at the University of São Paulo, Brazil, in 2003. He subsequently obtained a Ph.D. in Computer Science from the Federal University of Minas Gerais, Brazil, in 2007, and from the University of Cergy-Pontoise in France, in 2007. His research interests include image and video processing, computer vision, and machine learning

Dayse Garcia Miranda, Universidade Federal de Ouro Preto

PhD in Language Studies at CEFET, Brazil. Master’s degree in Education from the Federal University of Minas Gerais, Brazil. She pursued postgraduate studies in Inclusive Education at FJP/MG. She is a professor at the Federal University of Ouro Preto. Her areas of expertise include special education, inclusive education, and bilingualism.

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Published

2025-06-18

Issue

Vol. 29 No. 2 (2025): 29(2) 2025

Section

Articles

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