Detection and Classification of Facial Features Through the Use of Convolutional Neural Networks (CNN) in Alzheimer Patients

Please use this identifier to cite or link to this item: https://repositorio.uti.edu.ec//handle/123456789/3419

Title:	Detection and Classification of Facial Features Through the Use of Convolutional Neural Networks (CNN) in Alzheimer Patients
Authors:	Castillo-Salazar, David Varela-Aldás, José Borja-Galeas, Carlos Guevara-Maldonado, César Arias-Flores, Hugo Fierro-Saltos, Washington Rivera, Richard Hidalgo-Guijarro, Jairo Yandún-Velasteguí, Marco Lanzarini, Laura Gómez-Alvarado, Hector
Issue Date:	2020
Publisher:	Advances in Intelligent Systems and Computing. Volume 1026, Pages 619 - 625. 2nd International Conference on Human Systems Engineering and Design: Future Trends and Applications, IHSED 2019. Munich. 16 September 2019 through 18 September 2019
Abstract:	In recent years, the widespread use of artificial neural networks in the field of image processing has been of vital relevance to research. The main objective of this research work is to present an effective and efficient method for the detection of eyes, nose and lips in images that include faces of Alzheimer’s patients. The methods to be used are based on the extraction of deep features from a well-designed convolutional neural network (CNN). The result focuses on the processing and detection of facial features of people with and without Alzheimer’s disease. © Springer Nature Switzerland AG 2020.
URI:	https://link.springer.com/chapter/10.1007/978-3-030-27928-8_94 http://repositorio.uti.edu.ec//handle/123456789/3419
Appears in Collections:	Artículos Científicos Indexados

Files in This Item:

There are no files associated with this item.

This item is licensed under a Creative Commons License

DSpace JSPUI