Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/127883
Título: Novelty detection in human-machine interaction through a multimodal approach
Autores/as: Salas Cáceres, José Ignacio 
Lorenzo Navarro, José Javier 
Freire Obregón, David Sebastián 
Castrillón Santana, Modesto Fernando 
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Novelty detection
Human-machine interaction
Biometrics
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: 26th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2023
Resumen: As the interest in robots continues to grow across various domains, including healthcare, construction and education, it becomes crucial to prioritize improving user experience and fostering seamless interaction. These human-machine interactions (HMI) are often impersonal. Our proposal, built upon previous work in the field, aims to use biometric data of individuals to detect whether a person has been encountered before. Since many models depend on a threshold set, an optimization method using a genetic algorithm was proposed. The novelty detection is made through a multimodal approach using both voice and facial images from the individuals, although the unimodal approaches of just each single cue were also tested. To assess the effectiveness of the proposed system, we conducted comprehensive experiments on three diverse datasets, namely VoxCeleb, Mobio and AveRobot, each possessing distinct characteristics and complexities. By examining the impact of data quality on model performance, we gained valuable insights into the effectiveness of the proposed solution. Our approach outperformed several conventional novelty detection methods, yielding superior and therefore promising results.
URI: http://hdl.handle.net/10553/127883
ISBN: 978-3-031-49017-0
ISSN: 0302-9743
DOI: 10.1007/978-3-031-49018-7_33
Fuente: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14469, p. 464–479 (2023)
Colección:Actas de congresos
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