分离人脸频段，实现表情识别与隐私保护的平衡
Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation
- 分高低频处理：低频去身份，高频保表情
- 精度达78.84%，身份泄露率仅2.01%
- 适合需高隐私保障的表情识别场景
面部表情识别（FER）系统因可能暴露敏感身份信息而引发严重隐私担忧。本文研究在保留FER能力的同时消除身份信息。基于低频成分主要包含身份信息、高频成分捕捉表情的观察，提出一种新型双流框架，分别对各成分实施隐私增强。引入可控隐私增强机制以优化性能，并设计特征补偿器以强化任务相关特征而不损害隐私。此外，提出一种新的隐私-效用权衡指标，为闭集FER任务提供可量化的隐私保护效能评估。在基准数据集CREMA-D上的大量实验表明，该框架在实现78.84%识别准确率的同时，身份泄露率仅为2.01%，展现了其在安全可靠的视频表情识别应用中的潜力。
原文摘要 · Abstract (English)
Facial expression recognition (FER) systems raise significant privacy concerns due to the potential exposure of sensitive identity information. This paper presents a study on removing identity information while preserving FER capabilities. Drawing on the observation that low-frequency components predominantly contain identity information and high-frequency components capture expression, we propose a novel two-stream framework that applies privacy enhancement to each component separately. We introduce a controlled privacy enhancement mechanism to optimize performance and a feature compensator to enhance task-relevant features without compromising privacy. Furthermore, we propose a novel privacy-utility trade-off, providing a quantifiable measure of privacy preservation efficacy in closed-set FER tasks. Extensive experiments on the benchmark CREMA-D dataset demonstrate that our framework achieves 78.84% recognition accuracy with a privacy (facial identity) leakage ratio of only 2.01%, highlighting its potential for secure and reliable video-based FER applications.
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