Title: Employee Emotion Detection 🤖😃
Abstract: In my project, I addressed the increasing work stress and anxiety issues faced by employees in various workplaces. I developed a system that utilizes Artificial Intelligence techniques, including CNN and LSTM, to detect employees' emotions at work. By analyzing facial expressions, my system can identify emotions such as happiness, anger, fear, and more, allowing us to gauge the emotional index at the workplace.
Keywords: FER-2013, CNN, LSTM, one-shot learning, Emotion Detection using AI, Person identification using AI
Introduction: Emotion detection technology is transforming the way humans interact with intelligent systems. With the power of AI and computer vision, we can now recognize facial features and interpret emotional states. I explored the potential applications of this technology, from market research to healthcare and security, aiming to create a positive impact on the workplace environment.
Problem Statement: Employees often face physical and mental challenges at work, leading to high job turnover rates. To combat this issue, my project focuses on developing a system that can detect emotions in real-time, helping organizations create a safer and more supportive work environment.
Proposed Solution: My emotion detection system utilizes machine learning algorithms in the field of computer vision to detect and track human emotions. It can be deployed in offices, universities, and busy places, providing valuable insights into employee emotional states. This system can be used for educational purposes, lying detection, emotional research, and even clever marketing strategies.
Project Scope: The scope of my project is extensive, aiming to benefit employees in various work environments. By accurately detecting and analyzing emotions, we can reduce job turnover and enhance overall work performance. The system will be eco-friendly and can be implemented in multiple organizations.
Project Objectives:
Reduce job turnover by creating a safe work environment. Develop a web application for emotion detection. Achieve an accurate emotion prediction rate of around 85%. Stakeholders: My project's stakeholders include organization owners, users who register their organizations on the application, and the development team behind this innovative system.
I'm proud of the work I've done, and I'm excited to see the positive impact it can have on workplaces and employees. Special thanks to my mentors and teammates for their support throughout this journey.