A LIGHTWEIGHT FACE RECOGNITION SYSTEM FOR MEDIUM‑SCALE DATASETS: ALGORITHM SELECTION AND VECTOR DATABASE IMPLEMENTATION
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Abstract
Face recognition is widely used for access control, attendance, and surveillance, yet many real systems are medium‑scale (10k–50k images) rather than billion‑scale. This study examines algorithm choice for a 13,000‑image system (similar size to LFW), comparing classic methods (Eigenfaces, Fisherfaces, LBPH) with modern deep models (FaceNet, ArcFace, SFace). We evaluate accuracy, runtime, model footprint, and deployment simplicity. Results show SFace — a compact CNN that yields 128‑D embeddings — provides the best balance for medium deployments, exceeding 95% accuracy on LFW while occupying just 37 MB and running effectively on CPU. We also show a straightforward in‑memory vector store using cosine similarity can serve 13,000 embeddings with sub‑millisecond queries. A full reference implementation using OpenCV and NumPy is included. These findings give practical guidance for developers selecting face recognition solutions optimized for accuracy, efficiency, and ease of deployment in medium‑scale production environments.
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