Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis
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 t...
- Autores:
-
Galeano Ruiz, Melissa
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/43459
- Acceso en línea:
- https://hdl.handle.net/10495/43459
- Palabra clave:
- 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
- Rights
- embargoedAccess
- License
- https://creativecommons.org/licenses/by-nc-sa/4.0/
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Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis |
| title |
Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis |
| spellingShingle |
Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis 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 |
| title_short |
Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis |
| title_full |
Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis |
| title_fullStr |
Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis |
| title_full_unstemmed |
Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis |
| title_sort |
Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis |
| dc.creator.fl_str_mv |
Galeano Ruiz, Melissa |
| dc.contributor.advisor.none.fl_str_mv |
Torres López, Edwar Andrés Alaeddini, Adel |
| dc.contributor.author.none.fl_str_mv |
Galeano Ruiz, Melissa |
| dc.subject.decs.none.fl_str_mv |
Neural Networks, Computer Redes Neurales de la Computación |
| topic |
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 |
| dc.subject.unesco.none.fl_str_mv |
Innovation Innovación Artificial intelligence Inteligencia artificial |
| dc.subject.lemb.none.fl_str_mv |
Face perception Percepción de caras Robots, industrial Robots industriales |
| dc.subject.proposal.spa.fl_str_mv |
Detección de emociones Visión por computadora |
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http://vocabularies.unesco.org/thesaurus/concept17170 http://vocabularies.unesco.org/thesaurus/concept3052 |
| dc.subject.meshuri.none.fl_str_mv |
https://id.nlm.nih.gov/mesh/D016571 |
| description |
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. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-11-13T20:14:06Z |
| dc.date.available.none.fl_str_mv |
2024-11-13T20:14:06Z |
| dc.date.issued.none.fl_str_mv |
2024 |
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Tesis/Trabajo de grado - Monografía - Pregrado |
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http://purl.org/coar/resource_type/c_7a1f |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/draft |
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draft |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10495/43459 |
| url |
https://hdl.handle.net/10495/43459 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
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https://n9.cl/cnn_emotion_recognition_eng |
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58 páginas |
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application/pdf |
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Universidad de Antioquia |
| dc.publisher.place.spa.fl_str_mv |
Medellín, Colombia |
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Facultad de Ingeniería. Ingeniería Mecánica |
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Torres López, Edwar AndrésAlaeddini, AdelGaleano Ruiz, Melissa2024-11-13T20:14:06Z2024-11-13T20:14:06Z2024https://hdl.handle.net/10495/43459ABSTRACT : 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.PregradoIngeniera Mecánica58 páginasapplication/pdfengUniversidad de AntioquiaMedellín, ColombiaFacultad de Ingeniería. Ingeniería Mecánicahttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfEmpowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. 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