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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 | |
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GaleanoMelissa_2024_EmotionRecognitionEngineering.pdf Until 2025-11-13 | Trabajo de grado de pregrado | 2.02 MB | Adobe PDF | Visualizar/Abrir Request a copy |
Poster.pdf Until 2025-11-13 | Anexo | 775.09 kB | Adobe PDF | Visualizar/Abrir Request a copy |
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