Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/10495/43459
Título : Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis
Autor : Galeano Ruiz, Melissa
metadata.dc.contributor.advisor: Torres López, Edwar Andrés
Alaeddini, Adel
metadata.dc.subject.*: Neural Networks, Computer
Redes Neurales de la Computación
Innovation
Innovación
Artificial intelligence
Inteligencia artificial
Face perception
Percepción de caras
Robots, industrial
Robots industriales
Detección de emociones
Visión por computadora
http://vocabularies.unesco.org/thesaurus/concept17170
http://vocabularies.unesco.org/thesaurus/concept3052
https://id.nlm.nih.gov/mesh/D016571
Fecha de publicación : 2024
Resumen : ABSTRACT : In the realm of mechanical engineering, seamless human-machine interaction is pivotal for innovation and progress. This internship proposal aims to develop real-time facial and emotion recognition software, addressing a critical need in the field. Such technology holds vast potential to transform mechanical engineering applications, from enhancing automotive safety systems to optimizing human-robot collaboration in industrial environments. At its core, the project focuses on creating a robust software solution utilizing convolutional neural networks (CNNs) and computer vision techniques, leveraging TensorFlow, OpenCV, NumPy, and Scikit-learn libraries. This software will play a key role in a larger initiative dedicated to advancing human-machine interaction within mechanical engineering contexts. By tackling the challenge of real-time facial and emotion recognition through a structured approach encompassing data collection, model development, integration, and optimization, the software will be tailored to meet the demands of real-world scenarios. Thus, this internship proposal offers hands-on experience and skill development opportunities while contributing to the broader goal of driving innovation and excellence in mechanical engineering through cutting-edge technological solutions.
metadata.dc.relatedidentifier.url: https://n9.cl/cnn_emotion_recognition_eng
Aparece en las colecciones: Ingeniería Mecánica

Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
GaleanoMelissa_2024_EmotionRecognitionEngineering.pdf
  Until 2025-11-13
Trabajo de grado de pregrado2.02 MBAdobe PDFVisualizar/Abrir  Request a copy
Poster.pdf
  Until 2025-11-13
Anexo775.09 kBAdobe PDFVisualizar/Abrir  Request a copy


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