Institute of Art Design + Technology
Dún Laoghaire

Conor Weldon 

BSc [Hons] Creative Computing

I am a driven and ambitious computer science student currently pursuing a 1 to 1 degree at IADT. With a passion for innovation and technology, I am already making waves in the industry, working full-time as a technical specialist at Microsoft's MTC. In this role, I oversees experiences as a project manager, ensuring that projects are delivered on-time and on-budget, while also providing technical expertise to clients. In summary, I am a highly motivated and accomplished computer science student and professional who is making a name for themselves in the industry. With a strong technical background, I am poised for continued success in my career and academic pursuits.

Artificial Intelligence

The code recognizes emotions and faces in a webcam video stream using OpenCV. It loads pre-trained face, eye, and smile detection models from OpenCV and then opens a connection to the default webcam. It captures frames from the video stream, displays them, and processes each detected face. For each face, it draws a rectangle around it, crops the face region from the frame, and then detects eyes and smiles in the face region using the eye and smile detection models.

If both eyes and a smile are detected, it draws rectangles around the eyes and the smile, and then searches for crow's feet or wrinkles around the outer corners of the eyes. If crow's feet are detected, it draws a green circle around the eye. The code now has been modified to calculate the confidence percentage based on the presence of a smile and crows feet around the eyes. The confidence percentage is added to the text displayed next to the recognized face, which is obtained from the name parameter passed to the recognizeemotionand_face() function.

Emotional Facial Recognition System

This thesis project aims to develop an Emotional Facial Recognition System (EFRS) using Artificial Intelligence (AI) and Machine Learning (ML) techniques. The EFRS system utilizes image processing techniques and facial recognition algorithms to detect emotions from facial expressions. The project focuses on developing a modular architecture that enables integration with various facial recognition algorithms and AI models. In addition, the project involved designing and building a physical component - a smart mirror - to provide a user-friendly interface for the system. The project also includes various testing methodologies, including functional testing, unit testing, and user testing, to ensure the system's reliability, accuracy, and usability. The system also tracks, graphs, and saves the data into CSV, Excel and PKL files for later analysis and manipulation. Overall, this thesis project aims to provide an effective and efficient system for detecting emotions in real-time, which can have numerous applications in various fields, such as healthcare, education, and marketing.