A real-time facial emotion recognition web app using a custom-trained CNN on the FER-2013 dataset. Built with Flask, OpenCV, and PyTorch.

A real-time facial analysis platform built with Flask, OpenCV, TensorFlow, PyTorch, and Next.js, featuring live face detection, age & gender estimation, and emotion recognition. Designed for robotics club events, tech fairs, and interactive AI demos, with a futuristic cyberpunk UI powered by Arwes.

A deep learning–based Facial Emotion Recognition System built using PyTorch and deployed with a Flask web application. Detects emotions like Happy, Sad, Angry, Neutral, Fear, and Surprise from images or webcam captures.

Full-stack adaptive learning platform with role-based workflows, real-time facial emotion recognition via a custom PyTorch Transformer (61.7% on AffectNet 8-class), and emotion-driven game difficulty adaptation. Stack: MERN, Flask, MediaPipe.

Facial Emotion Recognition AI detects emotions like happiness, sadness, and anger using deep learning and OpenCV. It uses a CNN model for real-time or image-based emotion classification. Features include pretrained models, OpenCV-based face detection, and API deployment support. Built with Python, TensorFlow/PyTorch, and Flask/FastAPI.

Healthcare Emotion Detection uses Flask for the web interface, OpenCV/OpenCV-Python-Headless and Pillow for image handling, FER for facial emotion recognition, and PyTorch/Torchvision with Ultralytics for deep learning. It detects emotions in real-time to help healthcare providers monitor patient well-being and enable timely, personalized care.

Hi, I'm Rana 👋 AI Software Engineering graduate passionate about building real-world AI applications. 🚀 Projects - AI Resume Screening System - Real-Time Facial Emotion Recognition 🛠 Tech Stack Python | TensorFlow | PyTorch | Flask | Docker 📫 Contact Email: ranaroslan@yahoo.com