Dynamic Facial Expression Recognition
13 papers with code • 3 benchmarks • 3 datasets
Libraries
Use these libraries to find Dynamic Facial Expression Recognition models and implementationsMost implemented papers
Deep Residual Learning for Image Recognition
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
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
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
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
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
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.