Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Facial Expression RecognitionFacial Expression Recognition (FER)Similar Papers 제목 키워드 기반
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition
Reliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a…
Facial Expression RecognitionFacial Expression Recognition (FER)Generative Adversarial NetworkImage Generation+2Fairly Private: Investigating The Fairness of Visual Privacy Preservation Algorithms
As the privacy risks posed by camera surveillance and facial recognition have grown, so has the research into privacy preservation algorithms. Among these, visual privacy preservation algorithms attempt to impart bodily …
FairnessNow You See Me, Now You Don't: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos
Face video anonymization is aimed at privacy preservation while allowing for the analysis of videos in a number of computer vision downstream tasks such as expression recognition, people tracking, and action recognition.…
Action RecognitionA Comparative Study on Synthetic Facial Data Generation Techniques for Face Recognition
Facial recognition has become a widely used method for authentication and identification, with applications for secure access and locating missing persons. Its success is largely attributed to deep learning, which levera…
Face RecognitionAU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition
Recognizing human emotion/expressions automatically is quite an expected ability for intelligent robotics, as it can promote better communication and cooperation with humans. Current deep-learning-based algorithms may ac…
Facial Expression RecognitionFacial Expression Recognition (FER)Representation Learning