aryan2909/face-clustering-pipeline

Unsupervised Face-Clustering pipeline

This project aims to provide a robust and efficient pipeline for clustering human faces in an unsupervised manner. The pipeline is designed to work with images and video frames, and can handle large datasets.

The pipeline uses state-of-the-art computer vision techniques, including face detection, feature extraction, and clustering algorithms. The face detection module can detect faces under different lighting conditions and poses, while the feature extraction module extracts high-dimensional feature vectors that capture the unique characteristics of each face.

The clustering module then groups the feature vectors into clusters based on their similarities, resulting in groups of faces that belong to the same individual. The pipeline also includes tools for visualizing the results and evaluating the performance of the clustering algorithm.

The project is implemented in Python, using popular libraries such as OpenCV, scikit-learn, and Matplotlib. The code is well-documented and follows best practices for readability and maintainability.