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Real-Time Facial Expression Emoji Masking with Convolutional Neural Networks and Homography

2020-12-24 21:25:48
Qinchen Wang, Sixuan Wu, Tingfeng Xia
arXiv_CV
           
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: this https URL) Our results show that this pipeline is deploy-able in real-time, and is usable in educational settings.

Abstract (translated)

URL

https://arxiv.org/abs/2012.13447

PDF

https://arxiv.org/pdf/2012.13447.pdf

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