Large-Scale Face Clustering for Photo Archives
The Challenge
A massive archive of 12 million portraits was essentially a "digital shoebox," impossible to search or navigate by person. Manual tagging was financially out of the question, and off-the-shelf facial recognition failed on the low-quality, historical images.
Our Solution
Fine-tuned a FaceNet model on historical photos to generate 128-dimensional embeddings optimized for vintage photography. Used FAISS for efficient similarity search and hierarchical clustering with optimized distance thresholds. Incorporated active learning loops for ambiguous cases and implemented a human-in-the-loop review system.
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Results & Impact
Clustered 12M+ photos into ~800K distinct identities
Achieved 89% precision/recall on identity grouping
Reduced manual tagging effort by 90%
Enabled family history research at unprecedented scale
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