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Face Recognition

To recognize faces, PhotoPrism first extracts crops from your images using the Pigo face detection library. It is based on pixel intensity comparisons.

These are then fed into TensorFlow to compute 512-dimensional vectors for characterization.

In the final step, the DBSCAN algorithm attempts to cluster these so-called face embeddings so that they can be assigned to people with a few clicks.


We recommend that only advanced users and developers change these parameters.

Environment CLI Flag Default Description
PHOTOPRISM_FACE_SIZE --face-size 50 minimum size of faces in PIXELS (20-10000)
PHOTOPRISM_FACE_SCORE --face-score 9.000000 minimum face QUALITY score (1-100)
PHOTOPRISM_FACE_OVERLAP --face-overlap 42 face area overlap threshold in PERCENT (1-100)
PHOTOPRISM_FACE_CLUSTER_SIZE --face-cluster-size 80 minimum size of automatically clustered faces in PIXELS (20-10000) sponsors only
PHOTOPRISM_FACE_CLUSTER_SCORE --face-cluster-score 15 minimum QUALITY score of automatically clustered faces (1-100) sponsors only
PHOTOPRISM_FACE_CLUSTER_CORE --face-cluster-core 4 NUMBER of faces forming a cluster core (1-100) sponsors only
PHOTOPRISM_FACE_CLUSTER_DIST --face-cluster-dist 0.640000 similarity DISTANCE of faces forming a cluster core (0.1-1.5) sponsors only
PHOTOPRISM_FACE_MATCH_DIST --face-match-dist 0.460000 similarity OFFSET for matching faces with existing clusters (0.1-1.5) sponsors only
  • A reasonable range for the similarity distance between face embeddings is between 0.60 and 0.70, with a higher value being more aggressive and leading to larger clusters with more false positives.
  • To cluster a smaller number of faces, you can reduce the kernel to 3 or 2 similar faces.

External Resources