Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/124081
Title: Deep learning for diagonal earlobe crease detection
Authors: Almonacid-Uribe, Sara L.
Santana Jaria, Oliverio Jesús 
Hernández-Sosa, Daniel 
Freire-Obregón, David 
UNESCO Clasification: 1203 Ciencia de los ordenadores
220990 Tratamiento digital. Imágenes
Keywords: Computer vision
Diagonal earlobe crease
DELC
Frank’s sign
Cardiovascular disease, et al
Issue Date: 2023
Publisher: SciTePress Digital Library 
Project: Interaccióny Re-Identificación de Personas Mediante Machine Learning, Deep Learningy Análisis de Datos Multimodal: Hacia Una Comunicación Más Natural en la Robótica Social 
Re-identificación mUltimodal de participaNtes en competiciones dEpoRtivaS 
Journal: International Conference On Pattern Recognition Applications And Methods
Conference: 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023)
Abstract: An article published on Medical News Today in June 2022 presented a fundamental question in its title: Can an earlobe crease predict heart attacks? The author explained that end arteries supply the heart and ears. In other words, if they lose blood supply, no other arteries can take over, resulting in tissue damage. Consequently, some earlobes have a diagonal crease, line, or deep fold that resembles a wrinkle. In this paper, we take a step toward detecting this specific marker, commonly known as DELC or Frank's Sign. For this reason, we have made the first DELC dataset available to the public. In addition, we have investigated the performance of numerous cutting-edge backbones on annotated photos. Experimentally, we demonstrate that it is possible to solve this challenge by combining pre-trained encoders with a customized classifier to achieve 97.7% accuracy. Moreover, we have analyzed the backbone trade-off between performance and size, estimating MobileNet as the most promising encoder.
URI: http://hdl.handle.net/10553/124081
ISBN: 978-989-758-626-2
ISSN: 2184-4313
DOI: 10.5220/0011644400003411
Source: In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM - V. 1, p. 74-81, 2023 , Lisbon, Portugal
Appears in Collections:Actas de congresos
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