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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.
This is a web application that takes different kind of inputs(real-time, image, video) from the user and display the emotion based on the facial expressions.
This repository consists of a project where deep learning algorithms have been used to analyze facial emotions of the students in the class in real time using Open CV. A web app has also been created using streamlit for demonstration purposes.
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.
Final submission project in Belajar Pengembangan Machine Learning by Dicoding Academy about Image Classification Facial Emotion With EfficientNetV2-S Tensorflow
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
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.