Empowering Mechanical Engineering: The Role of Convolutional Neural Networks in Facial and Emotion Recognition in Engineering Contexts. Undergraduate Thesis (2024)
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.