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.

翻译：面部表情识别（FER）系统因可能暴露敏感身份信息而引发重大隐私担忧。本文研究如何在保持FER能力的同时去除身份信息。基于低频成分主要包含身份信息而高频成分捕捉表情的观察，我们提出了一种新颖的双流框架，分别对每个成分进行隐私增强处理。我们引入了一种可控隐私增强机制以优化性能，以及一个特征补偿器来增强任务相关特征而不损害隐私。此外，我们提出了一种新颖的隐私-效用权衡方法，为闭集FER任务中的隐私保护效果提供了可量化的度量。在基准CREMA-D数据集上进行的大量实验表明，我们的框架实现了78.84%的识别准确率，而隐私（面部身份）泄露率仅为2.01%，突显了其在安全可靠的基于视频的FER应用中的潜力。