Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866913595692941312 |
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| author | Xu, Feng Ahmedt-Aristizabal, David Petersson, Lars Wang, Dadong Li, Xun |
| author_facet | Xu, Feng Ahmedt-Aristizabal, David Petersson, Lars Wang, Dadong Li, Xun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00277 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation Xu, Feng Ahmedt-Aristizabal, David Petersson, Lars Wang, Dadong Li, Xun Computer Vision and Pattern Recognition 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. |
| title | Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.00277 |