Face Detection and Emotion Recognition models to capture and interpret facial expressions

An emotion driven movie recommendation system.

This GitHub repository hosts a Facial Emotion Recognition project that utilizes Convolutional Neural Networks (CNNs) to detect emotions from facial expressions in real-time. Built with Python, TensorFlow, Keras, and OpenCV, the project includes scripts for training the emotion detection model using the FER 2013 dataset and testing it with live webc

A ready-to-use Facial Expression Recognition model using MobileNet on augmented FER2013 dataset. Val accuracy > 89%

ICPR 2020: Facial Expression Recognition using Residual Masking Network

An emotion detection CNN-based model that can detect emotions from images in real-time

Tensorflow Implementation of DeepEmotion for Facial Expression Recognition

Facial Expression Recognition with a deep neural network as a PyPI package

A Face Emotion Recognizer

facEmotion is a academic project. it intergrate a custom built pure cnn based facial emotion recogtion model with accuracy of 64% in a web that implements technology like webRTC and asunchronous js.

A real-time application using Python and OpenCV for detecting and color-coding human emotions live via webcam. Utilizes the RMN model on FER-2013 dataset, made useful for user interaction.

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

A Federated Learning Platform For Facial Expression Recognition using the Flower framework and FER2013 dataset.

Emotion classifier implemented in pytorch

Examining different architectures of famous artificial intelligence networks using fer2013 dataset

A repository for Facial Emotion Recognition using CNN and personalized Song recommendations based on the mood of the user

Graduation project

Facial Emotion Recognition Project: A custom ResNet18 implementation using Keras with Random Erasing data augmentation on the FER2013 dataset with FER+ annotation.

Este repositorio se enfoca en el desarrollo de un sistema basado en redes neuronales para detectar emociones en estudiantes universitarios en el Perú. El objetivo principal es mejorar la calidad de la educación y el rendimiento de los estudiantes al proporcionar información inteligente sobre sus emociones utilizando redes neuronales.