监控视频人脸识别：人脸嵌入比较的最优精度指标与高效方案问询

Hey there! Let's dive into your questions about optimizing face embedding comparison for your CCTV footage use case—since you've already got Euclidean and Cosine distance working, we can build on that.

Q1: What's the most accurate metric for face embedding comparison, and how do I set its threshold? #

First off, there’s no one-size-fits-all "most accurate" metric—it depends on how your face embeddings are generated:

• If you’re using pre-trained models like FaceNet, ArcFace, or InsightFace, their embeddings are almost always output as normalized vectors (L2 norm = 1). In this case, normalized Euclidean distance and Cosine distance are mathematically related (the squared Euclidean distance equals 2*(1 - Cosine similarity)), so they’ll give identical ranking results for matching faces.
• For non-normalized embeddings, Cosine distance is often more robust to variations in embedding magnitude (which can happen if the model isn’t trained with L2 normalization), making it a safer bet for accuracy.

Threshold Setting (Critical for Your CCTV Scenario) #

Never use arbitrary thresholds—you need to tune this using your own CCTV-specific dataset:

1. Curate sample pairs: Gather a set of positive pairs (same person, different CCTV frames, accounting for lighting/angle changes) and negative pairs (different people).
2. Calculate distances: Run your chosen metric on all pairs to get a distribution of positive and negative distance values.
3. Analyze with ROC/DET curves: Plot a Receiver Operating Characteristic (ROC) curve to find the threshold that balances your desired False Positive Rate (FPR) and True Positive Rate (TPR). For example:
   • If minimizing false matches (critical for security) is priority, pick a threshold corresponding to FPR = 0.01 (1% chance of wrong match).
   • If you want to minimize missed matches, opt for a lower threshold with higher FPR (like 0.1).
4. Test in real scenarios: Validate the threshold on unseen CCTV footage to adjust for edge cases (e.g., blurry faces, partial occlusions).

Q2: What faster, effective methods exist besides Euclidean and Cosine distance? #

Your earlier attempts with KDTree and SVM might have underperformed because of misalignment with high-dimensional face embeddings (usually 128/256D). Here are better alternatives:

• Normalized Manhattan (L1) Distance:

  • Faster to compute than Euclidean distance (no square root operation)
  • For normalized embeddings, it delivers accuracy very close to Euclidean/Cosine distance—great for speed-critical applications.

• FAISS Approximate Nearest Neighbor (ANN) Algorithms:

  • Built specifically for high-dimensional vector retrieval, FAISS (Facebook AI Similarity Search) offers options like IVF-Flat (fast exact search for large datasets) or IVPQ (compressed vectors for even faster retrieval with minimal accuracy loss).
  • Perfect if you’re matching against a large database of faces (hundreds/thousands of people), as it avoids brute-force comparison of every embedding pair.

• HNSW (Hierarchical Navigable Small Worlds):

  • A state-of-the-art ANN algorithm that outperforms KDTree by a wide margin in high-dimensional spaces.
  • It’s efficient for both small and large datasets, and you can implement it easily with libraries like hnswlib.

• SVM for Pair Classification (Not Distance Metric):

  • If you want to revisit SVM, don’t use it as a distance metric—train a binary SVM classifier on your positive/negative embedding pairs. The model will learn to distinguish matches vs non-matches directly, which can improve accuracy if you have enough labeled data.

Quick Note on Your Existing Code #

I noticed your Euclidean distance calculation normalizes embeddings first—this is great practice, as it aligns with how most modern face models are trained. Just remember that this makes the Euclidean distance results directly comparable to Cosine distance for ranking.

内容的提问来源于stack exchange，提问作者Rishab P