Real-Time Facial Expression Emoji Masking with Convolutional Neural Networks and Homography
This work provides a real-time facial expression masking system for students in educational settings, which is an incremental application of existing technologies.
This paper presents a real-time system that detects facial expressions and masks the user's face with a corresponding emoji. The system integrates face detection, CNN-based expression categorization, and homography for emoji overlay, demonstrating deployability in real-time for educational settings.
Neural network based algorithms has shown success in many applications. In image processing, Convolutional Neural Networks (CNN) can be trained to categorize facial expressions of images of human faces. In this work, we create a system that masks a student's face with a emoji of the respective emotion. Our system consists of three building blocks: face detection using Histogram of Gradients (HoG) and Support Vector Machine (SVM), facial expression categorization using CNN trained on FER2013 dataset, and finally masking the respective emoji back onto the student's face via homography estimation. (Demo: https://youtu.be/GCjtXw1y8Pw) Our results show that this pipeline is deploy-able in real-time, and is usable in educational settings.