Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/124351
Título: Towards facial expression robustness in multi-scale wild environments
Autores/as: Freire Obregón, David Sebastián 
Hernández Sosa, José Daniel 
Santana Jaria, Oliverio Jesús 
Lorenzo Navarro, José Javier 
Castrillón Santana, Modesto Fernando 
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Facial expressions
Multi-scale resolution
Sequence-based approach
Recognition
Emotion, et al.
Fecha de publicación: 2023
Editor/a: Springer 
Proyectos: Interaccióny Re-Identificación de Personas Mediante Machine Learning, Deep Learningy Análisis de Datos Multimodal: Hacia Una Comunicación Más Natural en la Robótica Social 
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: 22th International Conference on Image Analysis and Processing (ICIAP 2023) 
Resumen: Facial expressions are dynamic processes that evolve over temporal segments, including onset, apex, offset, and neutral. However, previous works on automatic facial expression analysis have mainly focused on the recognition of discrete emotions, neglecting the continuous nature of these processes. Additionally, facial images captured from videos in the wild often have varying resolutions due to fixed-lens cameras. To address these problems, our objective is to develop a robust facial expression recognition classifier that provides good performance in such challenging environments. We evaluated several state-of-the-art models on labeled and unlabeled collections and analyzed their performance at different scales. To improve performance, we filtered the probabilities provided by each classifier and demonstrated that this improves decision-making consistency by more than 10%, leading to accuracy improvement. Finally, we combined the models’ backbones into a temporal-sequence classifier, leveraging this consistency-performance trade-off and achieving an additional improvement of 9.6%.
URI: http://hdl.handle.net/10553/124351
ISBN: 978-3-031-43147-0
ISSN: 0302-9743
DOI: 10.1007/978-3-031-43148-7_16
Fuente: ICIAP 2023: Image Analysis and Processing. Lecture Notes in Computer Science, [ISSN 0302-9743], vol 14233, p. 184–195 (September,2023)
Colección:Actas de congresos
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