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Bibliographic Details
Main Authors: Waldner, Dylan, Mitra, Shyamal
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2412.01860
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author Waldner, Dylan
Mitra, Shyamal
author_facet Waldner, Dylan
Mitra, Shyamal
contents This study takes a preliminary step toward teaching computers to recognize human emotions through Facial Emotion Recognition (FER). Transfer learning is applied using ResNeXt, EfficientNet models, and an ArcFace model originally trained on the facial verification task, leveraging the AffectNet database, a collection of human face images annotated with corresponding emotions. The findings highlight the value of congruent domain transfer learning, the challenges posed by imbalanced datasets in learning facial emotion patterns, and the effectiveness of pairwise learning in addressing class imbalances to enhance model performance on the FER task.
format Preprint
id arxiv_https___arxiv_org_abs_2412_01860
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Pairwise Discernment of AffectNet Expressions with ArcFace
Waldner, Dylan
Mitra, Shyamal
Computer Vision and Pattern Recognition
This study takes a preliminary step toward teaching computers to recognize human emotions through Facial Emotion Recognition (FER). Transfer learning is applied using ResNeXt, EfficientNet models, and an ArcFace model originally trained on the facial verification task, leveraging the AffectNet database, a collection of human face images annotated with corresponding emotions. The findings highlight the value of congruent domain transfer learning, the challenges posed by imbalanced datasets in learning facial emotion patterns, and the effectiveness of pairwise learning in addressing class imbalances to enhance model performance on the FER task.
title Pairwise Discernment of AffectNet Expressions with ArcFace
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2412.01860