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Refresh. Images of people showing eight different emotions, face dataset.
Facial Emotion Recognition Dataset. The dataset consists of images capturing people displaying 7 distinct emotions ( anger, contempt, disgust, fear, happiness, sadness and surprise ).
Face emotion recognition technology detects emotions and mood patterns invoked in human faces. This technology is used as a sentiment analysis tool to identify the six universal expressions, namely, happiness, sadness, anger, surprise, fear and disgust. Identifying facial expressions has a wide range of applications in human social interaction d…
Explore and run machine learning code with Kaggle Notebooks | Using data from Facial Expression Recognition (FER)Challenge.
Emotion Detection Using Yolo-V5 and RepVGG. This repository uses Yolo-V5 and RepVGG to detect facial expressions and classify emotions (see the architecture for more info on how it works). To see how to use the code, check out the usage section for more information.
The facial features extracted by these models lead to the state-of-the-art accuracy of face-only models on video datasets from EmotiW 2019, 2020 challenges: AFEW (Acted Facial Expression In The Wild), VGAF (Video level Group AFfect), EngageWild; and ABAW CVPR 2022 and ECCV 2022 challenges: Learning from Synthetic Data (LSD) and Multi-task ...
Aug 22, 2023 · Face Emotion Recognition Dataset (FER+) The FER+ dataset is a notable extension of the original Facial Expression Recognition (FER) dataset. Developed to improve upon the limitations of the original dataset, FER+ offers a more refined and nuanced labeling of facial expressions.
