Face Clustering Algorithm

Automatically organizes thousands of unlabeled images into distinct identity groups using high-dimensional vector analysis.

This process converts facial landmarks into 128-dimensional embeddings (numerical signatures) to measure similarity across massive datasets. By applying unsupervised methods like DBSCAN or Chinese Whispers, the system clusters these vectors based on Euclidean distance: grouping identical faces while filtering out outliers. It is the core logic in Google Photos and digital forensics: a standard workstation can sort 5,000 photos into unique folders in under three minutes with high precision.

https://github.com/ageitgey/face_recognition