Adaptive Multi-Lens Phase Modulation for Scale-Aware Privacy-Preserving Human Pose Recognition

Abstract

Recently, optical privacy protection has emerged as a promising approach for safeguarding visual privacy at the physical acquisition stage. However, existing methods often face a trade-off between privacy strength and human pose recognition accuracy, particularly in long-range and multi-scale scenarios. To address this challenge, we propose a novel adaptive optical privacy-preserving framework that integrates a learnable optical modulation system with a human pose recognition network. The core of our method lies in a sparse-weighted multi-lens model, where a lightweight multilayer perceptron (MLP) predicts a sparse set of coefficients to linearly combine predefined lens phase profiles based on facial region geometry. This enables dynamic control over the point spread function (PSF), adapting the degree of image degradation to subject scale in real time. Additionally, we introduce a privacy-aware loss function that selectively reduces facial localization accuracy while preserving body pose information. Extensive experiments on MSCOCO and FLIC datasets demonstrate that the proposed method achieves a favorable balance between privacy protection and pose estimation, outperforming previous optical- and software-based baselines.