无身份标签下保护隐私的同时保持表情识别准确率
Knowledge Priors for Identity-Disentangled Open-Set Privacy-Preserving Video FER
- 利用视频内与跨视频知识先验训练去身份网络
- 在开放集设置下实现隐私保护且表情识别准确率接近有监督方法
- 无需标注身份即可验证隐私鲁棒性，适合真实场景应用
面部表情识别依赖于暴露身份的面部数据，引发严重隐私问题。现有隐私保护方法在身份未知的开放集视频场景中表现不佳。本文提出一种两阶段框架，在无需任何身份标签的开放集视频设置下实现隐私保护的表情识别。首先，基于真实视频中的视频内与跨视频知识先验，训练去身份网络以消除身份信息，同时保留表情特征。随后，通过去噪模块恢复表达相关信号，提升表情识别性能。此外，提出基于伪造的验证方法，利用识别先验在无标注身份条件下严格评估隐私鲁棒性。在三个视频数据集上的实验表明，该方法在有效保护隐私的同时，表情识别准确率接近有身份监督的基线方法。
原文摘要 · Abstract (English)
Facial expression recognition relies on facial data that inherently expose identity and thus raise significant privacy concerns. Current privacy-preserving methods typically fail in realistic open-set video settings where identities are unknown, and identity labels are unavailable. We propose a two-stage framework for video-based privacy-preserving FER in challenging open-set settings that requires no identity labels at any stage. To decouple privacy and utility, we first train an identity-suppression network using intra- and inter-video knowledge priors derived from real-world videos without identity labels. This network anonymizes identity while preserving expressive cues. A subsequent denoising module restores expression-related information and helps recover FER performance. Furthermore, we introduce a falsification-based validation method that uses recognition priors to rigorously evaluate privacy robustness without requiring annotated identity labels. Experiments on three video datasets demonstrate that our method effectively protects privacy while maintaining FER accuracy comparable to identity-supervised baselines.
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