Facial Emotion Recognition using OpenCV and Deepface

The repo contains an audio emotion detection model, facial emotion detection model, and a model that combines both these models to predict emotions from a video

An Android app for real-time facial emotion recognition, designed to improve accuracy for Middle Eastern faces and women wearing hijabs. The CNN model is trained on a hybrid dataset (FER2013, CK+, JAFFE, and IEFDB), achieving 88% accuracy on the hybrid test set and 90% on IEFDB test set.

Real-time facial emotion detection using optimized CNN architecture achieving ~82% accuracy. Built with TensorFlow/Keras for production deployment with grayscale optimization and data augmentation techniques. Outperforms transfer learning models while maintaining edge-compatible efficiency.

A comprehensive real-time emotion recognition system combining facial and textual analysis with Furhat robot integration for social robotics applications.

Emotion detection using video data to analyze user emotions during the use of a certain website in order to improve user satisfaction

Final submission project in Belajar Pengembangan Machine Learning by Dicoding Academy about Image Classification Facial Emotion With EfficientNetV2-S Tensorflow

A powerful face recognition and analysis library for PHP using various models, with support for file paths, base64 strings, and data URLs.

A website that uses ML-algorithms to detect the facial patterns of a person and detect the identity and facial emotion of the person. The tech-stack includes html, css, js and django

A deep learning project that uses a Convolutional Neural Network (CNN) to automatically recognize human facial expressions from images. The model is trained on labeled facial emotion datasets to classify emotions such as happy, sad, angry, surprised, and more with high accuracy.

Facial Emotion Detection; It can detect and categorize facial emotions according to pictures. Powered by; IBM Watson Visual Recognition Services.

Video analysis system that extracts emotional patterns, audio characteristics, and visual elements from trailers and other content. Provides data-driven feedback on narrative flow, emotional engagement, pacing, and predictability through a pipeline of specialized Jupyter notebooks.