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

ICPR 2020: Facial Expression Recognition using Residual Masking Network

Deep facial expressions recognition using Opencv and Tensorflow. Recognizing facial expressions from images or camera stream

A landmark-driven method on Facial Expression Recognition (FER)

Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset

real-time face detection and emotion classification

Lightweight Facial Expression(emotion) Recognition model

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

Images and Videos, Real-time Facial Expession Recognition Application with Combine CNN , deep learning features extraction incorporate SIFT, FAST feature .

A facial emotion recognition program implemented in Python using TensorFlow, Keras and OpenCV and trained on the FER2013 dataset with FERPlus' labels.

Engagement Detection, including facial detection and emotion recognition, using CNNs/LSTMs.

A Deep Learning application to recognize emotion from facial expressions.

We present our facial expression recognition models for fer-2013 dataset

emotion classification using fer2013 datasets with a Tensorflow CNN model.

Conditional Cycle-Consistent Generative Adversarial Networks (CCycleGAN)

This project is a real-time facial emotion recognition system using OpenCV, Mediapipe, and DeepFace. It captures video from a webcam, detects facial landmarks, and analyzes emotions in real-time using deep learning models.

Deep Learning -Facial expression recognition -caffe -fer2013

Using Keras (Tensorflow), CNN and OpenCV, this model accurately identifies emotions from facial expressions in real-time video streams.

A human emotion recognition based learning assistant. HACKOH/IO - 2019

Graduation project