Real-Time Facial Expression Emoji Masking with Convolutional Neural Networks and Homography
Computer Vision and Pattern Recognition
2020-12-29 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2012.13447,
title = {Real-Time Facial Expression Emoji Masking with Convolutional Neural Networks and Homography},
author = {Qinchen Wang and Sixuan Wu and Tingfeng Xia},
journal= {arXiv preprint arXiv:2012.13447},
year = {2020}
}