Authors:
Yücel Çelik
and
Sezer Gören
Affiliation:
Department of Computer Engineering, Yeditepe University, Istanbul, Turkey
Keyword(s):
Facial Expression Detection, Face Masks, Covid-19, AI, Machine Learning, Convolutional Neural Network.
Abstract:
The usage of face masks has increased dramatically in recent years due to the pandemic. This made many systems that depended on a full facial analysis not as accurate on faces that are covered with a face mask, which may lead to errors in the system. In this paper, we propose a Convolutional Neural Network (CNN) model that was trained solely on face masks to be more accurate and on point, that could more easily determine facial expressions. Our CNN model was trained with a seven different expression category dataset that only had people with face masks. Although we could not find a suitable dataset with face masks, we opted to generate a synthetic one. The dataset generation was done using Python and the help of the OpenCV library. The process is, after finding the dimensions of the face, we Perspective Transform the face mask object to be able to overlay it on the face. After that, the CNN model was also generated using Python with a CNN model. Using this method we gathered favorabl
e results on the test subjects with 70.1% accuracy on the validation batch where previous facial expression recognition systems mostly failed to even recognize the face since they were not trained to recognize faces with face masks.
(More)