A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation

• URL: http://arxiv.org/abs/2201.12705v1
• Date: Sun, 30 Jan 2022 02:10:01 GMT
• Title: A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation
• Authors: Nyle Siddiqui, Rushit Dave, Tyler Bauer, Thomas Reither, Dylan Black, Mitchell Hanson
• Abstract summary: We propose a framework in which models developed for FER can be compared and contrasted against one another. A lightweight convolutional neural network is trained on the AffectNet dataset. A web application is developed and deployed with our proposed framework as a proof of concept.
• Score: 0.17398560678845074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial emotion recognition is a vast and complex problem space within the domain of computer vision and thus requires a universally accepted baseline method with which to evaluate proposed models. While test datasets have served this purpose in the academic sphere real world application and testing of such models lacks any real comparison. Therefore we propose a framework in which models developed for FER can be compared and contrasted against one another in a constant standardized fashion. A lightweight convolutional neural network is trained on the AffectNet dataset a large variable dataset for facial emotion recognition and a web application is developed and deployed with our proposed framework as a proof of concept. The CNN is embedded into our application and is capable of instant real time facial emotion recognition. When tested on the AffectNet test set this model achieves high accuracy for emotion classification of eight different emotions. Using our framework the validity of this model and others can be properly tested by evaluating a model efficacy not only based on its accuracy on a sample test dataset, but also on in the wild experiments. Additionally, our application is built with the ability to save and store any image captured or uploaded to it for emotion recognition, allowing for the curation of more quality and diverse facial emotion recognition datasets.

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