A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)
Code for the paper "Facial Emotion Recognition: State of the Art Performance on FER2013"
FERAtt: Facial Expression Recognition with Attention Net
Lightweight Facial Expression(emotion) Recognition model
Deep Attentive Center Loss
Apache MXNet Gluon implementation for state of the art FER+ paper for Facial Emotion Recognition - https://arxiv.org/abs/1608.01041
It's a project of facial emotion recognition.
A Pytorch Implementation of FER( facial expression recognition )
We present our facial expression recognition models for fer-2013 dataset
Recognising expression/emotion of unique faces in a video
This is an reimplement of the work Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition. This code is based on the original author's code with the following changes. Thanks, Kai Wang.
Project to find one of 9 pre-trained emotions on given photo, video or webcam-stream
This projected explored the effect of introducing channel and spatial attention mechanisms, namely SEN-Net, ECA-Net, and CBAM to existing CNN vision-based models such as VGGNet, ResNet, and ResNetV2 to perform the Facial Emotion Recognition task.
🔍 Face Scanning — a Flask application to detect facial expressions: smiling, neutral, and sad. Lightweight, neat, and ready to be used for UX prototypes, emotion testing, or just to check who’s really happy during the pitch. So, it’s not mind reading, but pretty close. 😏
Embedded realization of a FER-CNN(face emotion recogniton) based on STM32H7 MCU.
This project implements a Facial Emotion Recognition System using Convolutional Neural Networks (CNNs) built with Keras and TensorFlow. The model is trained on grayscale face images (48×48) and can detect seven emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise.
Build a Face Emotion Recognition (FER) Algorithm
Data Rate Change Algorithms for an Efficient HF Communication
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