Enhancing Facial Expression Recognition Under Data Uncertainty Based on Embedding Proximity

Facial Expression Recognition (FER) on unconstrained datasets poses a significant challenge, primarily due to data uncertainty stemming from human subjectivity and ambiguous facial expressions. Previous methods attempt to address this issue through relabeling strategies. However, this work reveals a...

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Veröffentlicht in:	IEEE access 2024, Vol.12, p.85324-85337
Hauptverfasser:	Chen, Ning, Kok, Ven Jyn, Seng Chan, Chee
Format:	Artikel
Sprache:	eng
Schlagworte:	Annotations Data uncertainty Embedding Face recognition Facial animation facial expression recognition feature embedding space Feature extraction Image color analysis Labeling Labels Noise Noise measurement Performance degradation Proximity relabeling Representations Training Uncertainty
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