Tackling facial emotion recognition (FER) tasks using DCNNs, VGG16 and Inception-V3 models

An academic research project for comparative analysis of deep learning models in facial emotion recognition.

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

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

This project implements deep learning models for classifying images. Using TensorFlow and Keras, it includes scripts and notebooks for training and testing neural networks on various datasets to achieve high accuracy in image categorization.

Product Market Analysis is a software that allows Companies to receive reviews on their products from Beta Testers by using Deep Learning to detect facial expressions.

A website that performs facial emotion analysis on uploaded images using AI!

A Deep Learning model deployed with FastAPI recognizes emotions using facial expression.

A implementation for facial expression recognition on fer2013 dataset using Residual Masking Network architecture

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.

The goal of facial expression detection is to accurately identify the emotions expressed by a person's face.

Facial expression recognition with FER dataset

Facial Emotion using FER13 dataset.

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

Emotion detection using convolutional neural networks and the fer2013 dataset.

A implementation for facial expression recognition on fer2013 dataset using a single convolutional neural network architecture

Efficient Ensemble Model for Facial Expression Recognition

Emotion and Voice Detection using Machine Learning Python Project. This Project about to detect human Voice and Facial emotion

Code accompanying the dissertation project for BEng Civil Engineering - Improving health and safety inductions on construction sites through the application of convolutional neural nets.