University of Florida
Facial Expression recognition
FACIAL EXPRESSION RECOGNITION
In an increasingly digital world, the ability to understand and express emotions through technology has become a pivotal aspect of human-computer interaction. The “Facial Expression Recognition” project was conceived to address this need by leveraging Convolutional Neural Networks (CNNs) to classify and interpret users’ facial expressions accurately.
The project recognizes the significance of non-verbal communication, especially in virtual settings, and aims to bridge the gap by transforming users’ facial expressions into emojis. By harnessing the power of deep learning and computer vision, this project intends to provide users with a more intuitive and expressive means of communicating emotions, ultimately enhancing their digital experiences.
ABOUT
The “Facial Expression Recognition” project represents a groundbreaking endeavor in the field of computer vision and human-computer interaction. By utilizing CNNs, our system is designed to analyze and classify users’ facial expressions in real time. This technology allows us to accurately identify a range of emotions, from joy to sadness, and seamlessly translate them into corresponding emojis.
Through this innovative approach, we aim to provide users with a novel and engaging way to convey their feelings in virtual environments, such as chat applications and online meetings. The project not only showcases the potential of deep learning in understanding human emotions but also strives to enhance the emotional depth of digital communication.
Year
2023
Client
Raven Studio
Services
Web Design
Project
Dynamic
Description
The “Facial Expression Recognition” project revolutionizes digital communication by using CNNs for real-time facial expression recognition and emoji translation. It enhances the emotional depth of virtual interactions in various applications.
I significantly improved system accuracy by applying CNNs for face detection and implementing rule-based processing using OpenCV, resulting in a notable 25% increase. Additionally, I engineered a Python system with Pandas, Keras, and OpenCV, achieving another 25% accuracy boost in recognizing facial expressions through optimized rules and convolutional networks.