Privacy-Preserving Audience Analytics: Lightweight Thermal Face Recognition for Real-Time Marketing Intelligence at the Edge

Abstract

Modern retail analytics demand real-time audience measurement, yet privacy regulations and consumer concerns limit traditional RGB cameras for demographic analysis. We present LTAS (Lightweight Thermal Architecture Search), a privacy-preserving framework that optimizes pre-trained models for edge-deployed thermal face recognition with minimal adaptation - requiring only single-batch fine-tuning on 64 images. Unlike expensive super-network approaches, LTAS leverages thermal imagery's constrained visual diversity to achieve rapid optimization. Evaluating 500 architectural variants across three thermal face datasets reveals that network depth reduction is the primary efficiency driver, achieving up to 48% parameter reduction while maintaining 82% of baseline accuracy. Depth optimization alone delivers 35-45% parameter reduction without accuracy loss, while kernel size modifications provide limited benefits. This enables real-time privacy-compliant audience analytics on resource-constrained retail devices, making thermal-based marketing measurement both practical and scalable.