Dynamic Facial Expression Recognition
12 papers with code • 3 benchmarks • 3 datasets
Libraries
Use these libraries to find Dynamic Facial Expression Recognition models and implementations
DFEW
MAFW
Most implemented papers
Deep Residual Learning for Image Recognition
tensorflow/models
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CVPR 2016
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
google-research/vision_transformer
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ICLR 2021
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
Learning Spatiotemporal Features with 3D Convolutional Networks
facebookarchive/C3D
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ICCV 2015
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset.
Former-DFER: Dynamic Facial Expression Recognition Transformer
zengqunzhao/Former-DFER
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ACM International Conference on Multimedia 2021
Specifically, the proposed Former-DFER mainly consists of a convolutional spatial transformer (CS-Former) and a temporal transformer (T-Former).
Clip-aware expressive feature learning for video-based facial expression recognition
CVLab-Liuyuanyuan/CEFLNet
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Information Sciences 2022
In this paper, we divide a video into several short clips for processing and propose a clip-aware emotion-rich feature learning network (CEFLNet) for robust video-based FER.
Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild
muse1998/IAL-for-Facial-Expression-Recognition
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One of the main reasons is that video sequences often contain frames with different expression intensities, especially for the facial expressions in the real-world scenarios, while the images in SFER frequently present uniform and high expression intensities.
Rethinking the Learning Paradigm for Dynamic Facial Expression Recognition
Dynamic Facial Expression Recognition (DFER) is a rapidly developing field that focuses on recognizing facial expressions in video format.
MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition
Dynamic facial expression recognition (DFER) is essential to the development of intelligent and empathetic machines.
Prompting Visual-Language Models for Dynamic Facial Expression Recognition
For the visual part, based on the CLIP image encoder, a temporal model consisting of several Transformer encoders is introduced for extracting temporal facial expression features, and the final feature embedding is obtained as a learnable "class" token.
From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos
And the TMAs capture and model the relationships of dynamic changes in facial expressions, effectively extending the pre-trained image model for videos.
