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photo-content-recognition-curation-expert Expert in photo content recognition, intelligent curation, and quality filtering. Specializes in face/animal/place recognition, perceptual hashing for de-duplication, screenshot/meme detection, burst photo selection, and quick indexing strategies. Activate on 'face recognition', 'face clustering', 'perceptual hash', 'near-duplicate', 'burst photo', 'screenshot detection', 'photo curation', 'photo indexing', 'NSFW detection', 'pet recognition', 'DINOHash', 'HDBSCAN faces'. NOT for GPS-based location clustering (use event-detection-temporal-intelligence-expert), color palette extraction (use color-theory-palette-harmony-expert), semantic image-text matching (use clip-aware-embeddings), or video analysis/frame extraction.
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2 archivos name photo-content-recognition-curation-expert description Expert in photo content recognition, intelligent curation, and quality filtering. Specializes in face/animal/place recognition, perceptual hashing for de-duplication, screenshot/meme detection, burst photo selection, and quick indexing strategies. Activate on 'face recognition', 'face clustering', 'perceptual hash', 'near-duplicate', 'burst photo', 'screenshot detection', 'photo curation', 'photo indexing', 'NSFW detection', 'pet recognition', 'DINOHash', 'HDBSCAN faces'. NOT for GPS-based location clustering (use event-detection-temporal-intelligence-expert), color palette extraction (use color-theory-palette-harmony-expert), semantic image-text matching (use clip-aware-embeddings), or video analysis/frame extraction. allowed-tools Read,Write,Edit,Bash,Grep,Glob,mcp__firecrawl__firecrawl_search,WebFetch category AI & Machine Learning tags ["face-recognition","deduplication","curation","indexing","nsfw"] pairs-with [{"skill":"event-detection-temporal-intelligence-expert","reason":"Temporal context for photos"},{"skill":"wedding-immortalist","reason":"Curate wedding photo collections"}]
Photo Content Recognition & Curation Expert
Expert in photo content analysis and intelligent curation. Combines classical computer vision with modern deep learning for comprehensive photo analysis.
When to Use This Skill
✅ Use for:
Face recognition and clustering (identifying important people)
Animal/pet detection and clustering
Near-duplicate detection using perceptual hashing (DINOHash, pHash, dHash)
Burst photo selection (finding best frame from 10-50 shots)
Screenshot vs photo classification
Meme/download filtering
NSFW content detection
Quick indexing for large photo libraries (10K+)
Aesthetic quality scoring (NIMA)
❌ NOT for:
GPS-based location clustering → event-detection-temporal-intelligence-expert
Color palette extraction → color-theory-palette-harmony-expert
Semantic image-text matching → clip-aware-embeddings
Video analysis or frame extraction
Quick Decision Tree
What do you need to recognize/filter?
│
├─ Duplicate photos? ─────────────────────────────── Perceptual Hashing
│ ├─ Exact duplicates? ──────────────────────────── dHash (fastest)
│ ├─ Brightness/contrast changes? ───────────────── pHash (DCT-based)
│ ├─ Heavy crops/compression? ───────────────────── DINOHash (2025 SOTA)
│ └─ Production system? ─────────────────────────── Hybrid (pHash → DINOHash)
│
├─ People in photos? ─────────────────────────────── Face Clustering
│ ├─ Known thresholds? ──────────────────────────── Apple-style Agglomerative
│ └─ Unknown data distribution? ─────────────────── HDBSCAN
│
├─ Pets/Animals? ─────────────────────────────────── Pet Recognition
│ ├─ Detection? ─────────────────────────────────── YOLOv8
│ └─ Individual clustering? ─────────────────────── CLIP + HDBSCAN
│
├─ Best from burst? ──────────────────────────────── Burst Selection
│ └─ Score: sharpness + face quality + aesthetics
│
└─ Filter junk? ──────────────────────────────────── Content Detection
├─ Screenshots? ───────────────────────────────── Multi-signal classifier
└─ NSFW? ──────────────────────────────────────── Safety classifier
Core Concepts
1. Perceptual Hashing for Near-Duplicate Detection
Problem: Camera bursts, re-saved images, and minor edits create near-duplicates.
Solution: Perceptual hashes generate similar values for visually similar images.
Method Comparison:
Method Speed Robustness Best For
dHash Fastest Low Exact duplicates pHash Fast Medium Brightness/contrast changes DINOHash Slower High Heavy crops, compression Hybrid Medium Very High Production systems
Hybrid Pipeline (2025 Best Practice):
Stage 1: Fast pHash filtering (eliminates obvious non-duplicates)Stage 2: DINOHash refinement (accurate detection)Stage 3: Optional Siamese ViT verification Hamming Distance Thresholds:
Conservative: ≤5 bits different = duplicates
Aggressive: ≤10 bits different = duplicates
→ Deep dive : references/perceptual-hashing.md
2. Face Recognition & Clustering Goal: Group photos by person without user labeling.
Apple Photos Strategy (2021-2025):
Extract face + upper body embeddings (FaceNet, 512-dim)
Two-pass agglomerative clustering
Conservative first pass (threshold=0.4, high precision)
HAC second pass (threshold=0.6, increase recall)
Incremental updates for new photos
No threshold tuning required
Robust to noise
Better for unknown data distributions
Setting Agglomerative HDBSCAN Pass 1 threshold 0.4 (cosine) - Pass 2 threshold 0.6 (cosine) - Min cluster size - 3 photos Metric cosine cosine
→ Deep dive : references/face-clustering.md
3. Burst Photo Selection Problem: Burst mode creates 10-50 nearly identical photos.
Criterion Weight Measurement Sharpness 30% Laplacian variance Face Quality 35% Eyes open, smiling, face sharpness Aesthetics 20% NIMA score Position 10% Middle frames bonus Exposure 5% Histogram clipping check
Burst Detection: Photos within 0.5 seconds of each other.
→ Deep dive : references/content-detection.md
4. Screenshot Detection Signal Confidence Description UI elements 0.85 Status bars, buttons detected Perfect rectangles 0.75 >5 UI buttons (90° angles) High text 0.70 >25% text coverage (OCR) No camera EXIF 0.60 Missing Make/Model/Lens Device aspect 0.60 Exact phone screen ratio Perfect sharpness 0.50 >2000 Laplacian variance
Decision: Confidence >0.6 = screenshot
→ Deep dive : references/content-detection.md
5. Quick Indexing Pipeline Goal: Index 10K+ photos efficiently with caching.
Perceptual hashes (de-duplication)
Face embeddings (people clustering)
CLIP embeddings (semantic search)
Color palettes
Aesthetic scores
Performance (10K photos, M1 MacBook Pro):
Operation Time Perceptual hashing 2 min CLIP embeddings 3 min (GPU) Face detection 4 min Color palettes 1 min Aesthetic scoring 2 min (GPU) Clustering + dedup 1 min Total (first run) ~13 min Incremental <1 min
→ Deep dive : references/photo-indexing.md
Common Anti-Patterns
Anti-Pattern: Euclidean Distance for Face Embeddings distance = np.linalg.norm(embedding1 - embedding2)
Why it's wrong: Face embeddings are normalized; cosine similarity is the correct metric.
from scipy.spatial.distance import cosine
distance = cosine(embedding1, embedding2)
Anti-Pattern: Fixed Clustering Thresholds What it looks like: Using same distance threshold for all face clusters.
Why it's wrong: Different people have varying intra-class variance (twins vs. diverse ages).
What to do instead: Use HDBSCAN for automatic threshold discovery, or two-pass clustering with conservative + relaxed passes.
Anti-Pattern: Raw Pixel Comparison for Duplicates is_duplicate = np.allclose(img1, img2)
Why it's wrong: Re-saved JPEGs, crops, brightness changes create pixel differences.
What to do instead: Perceptual hashing (pHash or DINOHash) with Hamming distance.
Anti-Pattern: Sequential Face Detection What it looks like: Processing faces one photo at a time without batching.
Why it's wrong: GPU underutilization, 10x slower than batched.
What to do instead: Batch process images (batch_size=32) with GPU acceleration.
Anti-Pattern: No Confidence Filtering for face in all_detected_faces:
cluster(face)
Why it's wrong: Low-confidence detections create noise clusters (hands, objects).
What to do instead: Filter by confidence (threshold 0.9 for faces).
Anti-Pattern: Forcing Every Photo into Clusters What it looks like: Assigning noise points to nearest cluster.
Why it's wrong: Solo appearances shouldn't pollute person clusters.
What to do instead: HDBSCAN/DBSCAN naturally identifies noise (label=-1). Keep noise separate.
Quick Start from photo_curation import PhotoCurationPipeline
pipeline = PhotoCurationPipeline()
index = pipeline.index_library('/path/to/photos' )
duplicates = index.find_duplicates()
print (f"Found {len (duplicates)} duplicate groups" )
face_clusters = index.cluster_faces()
print (f"Found {len (face_clusters)} people" )
best_photos = pipeline.select_best_from_bursts(index)
real_photos = pipeline.filter_screenshots(index)
collage_photos = pipeline.curate_for_collage(index, target_count=100 )
Python Dependencies torch transformers facenet-pytorch ultralytics hdbscan opencv-python scipy numpy scikit-learn pillow pytesseract
Integration Points
event-detection-temporal-intelligence-expert : Provides temporal event clustering for event-aware curationcolor-theory-palette-harmony-expert : Extracts color palettes for visual diversitycollage-layout-expert : Receives curated photos for assemblyclip-aware-embeddings : Provides CLIP embeddings for semantic search and DeepDBSCAN
References
DINOHash (2025) : "Adversarially Fine-Tuned DINOv2 Features for Perceptual Hashing"Apple Photos (2021) : "Recognizing People in Photos Through Private On-Device ML"HDBSCAN : "Hierarchical Density-Based Spatial Clustering" (2013-2025)Perceptual Hashing : dHash (Neal Krawetz), DCT-based pHash
Version : 2.0.0
Last Updated : November 2025
