Developer Tests OpenCV SFace Model for Face Recognition via Nearest Neighbor Search

A developer tested OpenCV Zoo's SFace model to evaluate whether face embeddings can reliably identify the same person across different images using nearest neighbor search. SFace converts a 112x112 face crop into a 128-dimensional numerical vector, and the experiment checked whether images of the same person cluster closer together than images of different people. Two datasets were used for comparison: the Olivetti Faces dataset with 400 grayscale images across 40 people, and a subset of the Labeled Faces in the Wild dataset with 400 color images under more real-world conditions. The test measured cosine similarity between same-person and different-person embedding pairs, and also compared results between small grayscale crops and aligned color face crops. The full experiment code is publicly available on GitHub and can be run locally using the mise and uv tools, requiring approximately 500 MB of free disk space.