A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset
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Updated
Jun 14, 2021 - Python
A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset
Deep facial expressions recognition using Opencv and Tensorflow. Recognizing facial expressions from images or camera stream
ICPR 2020: Facial Expression Recognition using Residual Masking Network
Facial Expression Recognition with a deep neural network as a PyPI package
Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset
A landmark-driven method on Facial Expression Recognition (FER)
Code for the paper "Facial Emotion Recognition: State of the Art Performance on FER2013"
real-time face detection and emotion classification
Facial Expression Recognition
A facial emotion recognition program implemented in Python using TensorFlow, Keras and OpenCV and trained on the FER2013 dataset with FERPlus' labels.
Images and Videos, Real-time Facial Expession Recognition Application with Combine CNN , deep learning features extraction incorporate SIFT, FAST feature .
emotion classification using fer2013 datasets with a Tensorflow CNN model.
Engagement Detection, including facial detection and emotion recognition, using CNNs/LSTMs.
Lightweight Facial Expression(emotion) Recognition model
Conditional Cycle-Consistent Generative Adversarial Networks (CCycleGAN)
Facial Expression Recognition Using CNN and Haar-Cascade
A Deep Learning application to recognize emotion from facial expressions.
Facial expression recognition using Pytorch on FER2013 dataset and create simple app with streamlit
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