manojyadav0/face-emotion-detection-using-deeplearning-and-opencv

Welcome to the Face Recognition Program repository! This project leverages advanced facial recognition algorithms using deep learning and OpenCV. The project involved training a model with LBPH algorithm to detect and classify emotions from facial expressions, achieving accurate emotion recognition through real-time video processing.

Project has the following stages:

1.Collecting Images with Expressions

2.Detecting Faces in the Images and saving them

3.Training our classifier on the Faces

4.Recognition of expression in new Images

Key Features:

Advanced Facial Feature Analysis: Utilizes state-of-the-art convolutional neural networks (CNNs) to detect and analyze unique facial features. Custom Training: Allows users to train the model with their own dataset, enhancing the system's accuracy for specific individuals. Real-time Face Recognition: Supports live face detection and recognition from camera feeds, providing immediate results. High Accuracy: Employs robust feature extraction and matching techniques to ensure reliable identification. Scalable and Extensible: Designed to handle large datasets and can be easily extended with new features or integrated into existing systems. User-Friendly Interface: Provides a simple command-line interface for training and recognition tasks, making it accessible even to non-experts. Getting Started

Prerequisites:

Ensure you have the following installed:

Python 3.x: The programming language used for the project.

OpenCV: A library for computer vision tasks.

TensorFlow: An end-to-end open-source platform for machine learning.

NumPy: A library for numerical computations in Python.

Matplotlib: A library for creating static, animated, and interactive visualizations in Python