Deep Convolutional Neural Network Based Facial Expression Recognition in the Wild

• URL: http://arxiv.org/abs/2010.01301v1
• Date: Sat, 3 Oct 2020 08:17:00 GMT
• Title: Deep Convolutional Neural Network Based Facial Expression Recognition in the Wild
• Authors: Hafiq Anas, Bacha Rehman, Wee Hong Ong
• Abstract summary: We have used a proposed deep convolutional neural network (CNN) model to perform automatic facial expression recognition (AFER) on the given dataset. Our proposed model has achieved an accuracy of 50.77% and an F1 score of 29.16% on the validation set.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes the proposed methodology, data used and the results of our participation in the ChallengeTrack 2 (Expr Challenge Track) of the Affective Behavior Analysis in-the-wild (ABAW) Competition 2020. In this competition, we have used a proposed deep convolutional neural network (CNN) model to perform automatic facial expression recognition (AFER) on the given dataset. Our proposed model has achieved an accuracy of 50.77% and an F1 score of 29.16% on the validation set.

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