Testing Face Embedding Nearest Neighbor Search with OpenCV SFace

Short summary

OpenCV's SFace model converts face images into 128-dimensional embeddings for efficient nearest-neighbor matching. Testing on Olivetti Faces and Labeled Faces in the Wild datasets achieved 80–82% top-1 accuracy distinguishing same-person images from different-person pairs. The approach is lightweight (~5.7ms per image) and practical as a component in LLM-based agents when combined with other identity signals.

• •SFace generates 128D embeddings from face crops for fast similarity search
• •Real-world testing: 80–82% top-1 accuracy, 92–95% top-5 accuracy
• •Lightweight, reproducible, and suitable for production face recognition pipelines

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