Face Expression Recognition with ensemble for InceptionV3, ResNet50, MobileNetV2

Derin öğrenme ile yüz görüntülerinden duygu analizini tespit eden program projem. fer2013 veri seti kullanılmıştır.

This project is a custom implementation of VGGNet trained to classify images of faces into 7 classes, representing different emotions.

💻🔍😄 Application that detects emotions via the webcam and displays a mask on the face of the corresponding emoji.

MONITOR THE EMPLOYEE'S EMOTIONAL WELL-BEING WITH PRE-TRAINED NEURAL NETWORK GOOGLENET USING THE FER2013 DATASET

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

Emotion Detection is a real-time facial expression recognition system built with TensorFlow and OpenCV. It classifies seven emotions—Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise—using a CNN trained on the FER-2013 dataset. The model supports both webcam-based and image-based emotion detection.

Emotion recognition from facial images using convolutional neural networks.

A set of Google colab notebooks with my work on data analysis

A real-time facial expression recognition system built with CNN, TensorFlow, and OpenCV. It uses a webcam to detect faces and classify emotions like happiness, sadness, anger, and more.

A Django-based web application that analyzes facial expressions using a webcam or uploaded images, providing personalized mental health suggestions powered by OpenAI. It leverages a CNN model trained on the FER2013 dataset to detect emotions in real-time and offer tailored advice.

Developed a deep learning–based system for facial recognition and emotion analysis. Explored classical computer vision methods by implementing face detection with Haar Cascade and face recognition using OpenCV’s LBPHFaceRecognizer. Additionally, trained a CNN on the FER-2013 dataset for 7-class emotion recognition.

A Convolutional Neural Network implemented using Tensorflow in order to achieve Human Emotion Recognition from a dataset of facial images

Facial Emotion Recognition using CNN on FER-2013 dataset

This project implements a real-time facial emotion detection system using a custom-trained Convolutional Neural Network (CNN) model on the FER-2013 dataset using tensorflow.js.

This repository inclused several applications of extracting and classifying features from face images.