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

利用Pytorch设计完成的基于卷积神经网络实现的面部表情识别项目 —— A facial expression recognition project based on convolution neural network designed by Pytorch 【Plus版本】：https://github.com/hexiang10/face-recognition-plus

Facial Expression Recognition Using Attentional Convolutional Network, Pytorch implementation

Official Pytorch Implementation of the paper, "SwinFace: A Multi-task Transformer for Face Recognition, Facial Expression Recognition, Age Estimation and Face Attribute Estimation"

😁[VCIP'19 Oral] Official PyTorch Implementation of Facial Motion Prior Networks for Facial Expression Recognition

🤗 Facial expression recognition with Pytorch (My First Project in 2018)

PyTorch version for the "Micro-expression Recognition Based on Facial Graph Representation Learning and Facial Action Unit Fusion"

Lightweight Facial Expression(emotion) Recognition model

PyTorch version for the "Dual-Dtream Shallow Networks for Facial Micro-Expression Recognition"

Facial expression recognition using Pytorch on FER2013 dataset and create simple app with streamlit

A Pytorch Implementation of FER( facial expression recognition )

A Pytorch implementation of "A Compact Deep Learning Model for Robust Facial Expression Recognition (CVPR 2018)".

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Facial expression recognition package built on Pytorch and FER+ dataset from Microsoft.

“AI Based Facial Emotion Detection”, developed using many machine learning algorithms including convolution neural networks (CNN) for a facial expression recognition task. The goal is to classify each facial image into one of the seven facial emotion categories considered in this study.Trained CNN models with different depth using gray-scale images from the Kaggle website.CNN models are developed in Pytorch and exploited Graphics Processing Unit (GPU) computation in order to expedite the training process. In addition to the networks performing based on raw pixel data,Hybrid feature strategy is employed by which trained a novel CNN model with the combination of raw pixel data and Histogram of Oriented Gradients (HOG) features. To reduce the over fitting of the models,different techniques are utilized including dropout and batch normalization in addition to L2 regularization. Cross validation is applied to determine the optimal hyper-parameters and evaluated the performance of the developed models by looking at their training histories. Visualization of different layers of a network is presented to show what features of a face can be learned by CNN models. Based on the emotion the program recommends the music for the user to up flit the mood.

This is an official implementation in PyTorch of PTH-Net: Dynamic Facial Expression Recognition without Face Detection and Alignment..

A PyTorch implementation of DeXpression for facial expression recognition.

A Facial Expression Recognition Classifier with Pytorch

PyTorch implementation of landmark-based facial expression recognition using Spatio-Temporal BiLinear Networks (ST-BLN)

A PyTorch Toolbox for Facial Expression Recognition

Facial Expression Recognition system built from scratch in PyTorch. Detects faces and classifies emotions from images, video, and live webcam. Features model interpretability via saliency maps to visualize key facial features. Includes real-time inference GUI and automated evaluation scripts for 64x64px input.

A Facial Expression Recognition system developed using PyTorch and OpenCV.

CNN (Convolutional neural network)-based facial expression recognition model (with pretrained weights) implemented with Pytorch

PyTorch code for 'Accurate and Reliable Facial Expression Recognition Using Advanced Softmax Loss with Fixed Weights' (IEEE Signal Processing Letters)

PyTorch DataLoader for FER-2013 Dataset, Facial Expression Recognition Challenge

Facial expression recognition by PyTorch.

This project is a deep learning-based facial expression recognition system implemented using PyTorch. It can classify facial expressions into seven different emotions: angry, disgust, fear, happy, neutral, sad, and surprise.

This repo is the official PyTorch implementation for the paper Domain Knowledge Enhanced Vision-Language Pretrained Model for Dynamic Facial Expression Recognition (ACM MM2024)..

Developed a facial emotion detection system using Python, TensorFlow or PyTorch, and OpenCV. Utilize CNNs to classify emotions like happiness, sadness, and anger from images or real-time video. Gain experience in image-based emotion recognition, training models for facial expression analysis, and integrating advanced neural network architectures.

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