feat: add face detection, recognition, and clustering

- Create face_embeddings table with pgvector Vector(128) + HNSW index
- Implement extract_faces task (YuNet detection + SFace recognition)
- Implement recluster_faces task (DBSCAN clustering → Tag(kind=face_cluster))
- Clusters are named "Person N" and get representative_photo_id
- cluster_id FK → tags.id, SET NULL on delete for merge/rename support

Migration 0005 creates the face_embeddings table.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/app/models/face_embedding.py

@@ -0,0 +1,24 @@
+"""
+Face embedding model — stores per-face detection + recognition vectors.
+
+cluster_id FKs to tags.id where kind='face_cluster'. Null means
+unclustered (will be assigned by recluster_faces).
+"""
+from sqlalchemy import Column, String, Float, ForeignKey, DateTime, func
+from sqlalchemy.dialects.postgresql import JSONB
+from pgvector.sqlalchemy import Vector
+import uuid
+
+from app.database import Base
+
+
+class FaceEmbedding(Base):
+    __tablename__ = 'face_embeddings'
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    photo_id = Column(String, ForeignKey('photos.id', ondelete='CASCADE'), nullable=False, index=True)
+    bbox = Column(JSONB)           # [x1, y1, x2, y2] normalized 0-1
+    vector = Column(Vector(128))   # SFace → 128-d
+    cluster_id = Column(String, ForeignKey('tags.id', ondelete='SET NULL'), nullable=True, index=True)
+    quality = Column(Float)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())