1. Cosine Similarity vs Other Metrics
Cosine similarity is commonly used and effective because it measures the angle between two vectors, which works well when the magnitudes aren’t as important as the direction (which is true for normalized embeddings). Alternatively, you could also use Euclidean distance—especially if your embeddings are not L2-normalized. Many real-world face recognition models prefer Euclidean distance after normalizing the encodings.
2. Scalability with 100,000+ Encodings
Comparing a test encoding against 100,000+ entries can be computationally expensive. To maintain sub-2-second response times, you’ll need to optimize the similarity search. Some techniques include:
Using FAISS (Facebook AI Similarity Search) for fast approximate nearest neighbor (ANN) search.
Reducing dimensionality using PCA before indexing.
Caching recent or frequent queries.
Building hierarchical or quantized indices.
These are essential when deploying at scale, especially when dealing with AI facial recognition systems optimized for real-time performance in enterprise environments. (← hyperlink this keyword phrase to your blog)
3. Generalization to New Employees
Great observation—this is where face embedding methods like yours outperform softmax classifiers. The idea is that you're not learning to classify known individuals, but rather to map facial images into a metric space where proximity reflects identity.
This generalizes well to unseen identities as long as the embedding space has been trained on diverse data. The more variation (age, ethnicity, lighting, pose) your training data has, the better it will generalize. It’s not a traditional classification task, so the model doesn’t need retraining—it just compares distances in the learned space.
If you're interested in understanding how these kinds of systems are deployed in production—including architectural decisions, database encoding management, and performance optimization—studying modern AI-powered face recognition pipelines and deployment practices can offer valuable clarity.