arXiv:2004.11823
공식 코드 · Papers with Code
Facial Expression Recognition with Deep Learning
원문 초록
One of the most universal ways that people communicate is through facial expressions. In this paper, we take a deep dive, implementing multiple deep learning models for facial expression recognition (FER). Our goals are twofold: we aim not only to maximize accuracy, but also to apply our results to the real-world. By leveraging numerous techniques from recent research, we demonstrate a state-of-the-art 75.8% accuracy on the FER2013 test set, outperforming all existing publications. Additionally, we showcase a mobile web app which runs our FER models on-device in real time.
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Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data
V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression Recognition
Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
Navigating Label Ambiguity for Facial Expression Recognition in the Wild
Unimodal and Multimodal Static Facial Expression Recognition for Virtual Reality Users with EmoHeVRDB
QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
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