People and Faces

Smriti detects faces during scanning, groups them into clusters (one cluster per person), and lets you name, merge, and review those clusters in the People view.

Running face processing

Face processing runs automatically during the initial scan and on any rescan when new photos are added. You can also trigger it manually from Settings.

Detection uses SCRFD (face localization) and GLinTR-100 (identity embedding), both running locally via ONNX Runtime.

Clustering pipeline

Clustering happens in two stages:

1. Gallery retrieval. Each new face is compared against the gallery of existing clusters using k-nearest-neighbor matching with confidence bands. High-confidence matches auto-assign. Ambiguous matches are queued for review. Low-confidence faces drop to stage 2.
2. Complete-link agglomerative. The remaining unresolved faces are clustered pairwise. If the number of unresolved faces exceeds 2,000 in a single pass, stage 2 is skipped for performance and those faces are routed to the ambiguous-review queue or matched against galleries by the rescue pass instead. The cap will be lifted in a future release once HNSW-based approximate clustering replaces complete-link.

After clustering, a post-pass unifies clusters that look like the same person split by lighting variance, and a rescue pass matches any remaining orphans against existing galleries with looser thresholds.

What you can do in People

• Rename a cluster by clicking its title.
• Open a person to see all photos they appear in.
• Merge two clusters into one (e.g. when the system split the same person across several clusters).
• Review ambiguous matches — the review deck walks you through faces the system wasn’t sure about, one at a time.

Tuning

• Face confidence in Settings controls the minimum detection score for a face to be considered valid.
• Clustering threshold in Settings controls how tolerant the system is about grouping visually similar faces. Lower values mean tighter clusters (more but smaller groups); higher values mean looser clusters (fewer but larger groups). The default works for most libraries.

Privacy

Face embeddings and detection crops never leave the device. See PRIVACY.md for the full data-flow description.