Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation

• URL: http://arxiv.org/abs/2412.00277v2
• Date: Tue, 03 Dec 2024 12:04:07 GMT
• Title: Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation
• Authors: Feng Xu, David Ahmedt-Aristizabal, Lars Petersson, Dadong Wang, Xun Li,
• Abstract summary: 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. We introduce a controlled privacy enhancement mechanism to optimize performance and a feature compensator to enhance task-relevant features without compromising privacy.
• Score: 24.619279669211842
• License:
• Abstract: 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.

Related papers

• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• Diff-Privacy: Diffusion-based Face Privacy Protection [58.1021066224765]
  In this paper, we propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding.
  arXiv  Detail & Related papers  (2023-09-11T09:26:07Z)
• A Randomized Approach for Tight Privacy Accounting [63.67296945525791]
  We propose a new differential privacy paradigm called estimate-verify-release (EVR) EVR paradigm first estimates the privacy parameter of a mechanism, then verifies whether it meets this guarantee, and finally releases the query output. Our empirical evaluation shows the newly proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning.
  arXiv  Detail & Related papers  (2023-04-17T00:38:01Z)
• How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
  We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
  arXiv  Detail & Related papers  (2022-11-18T11:39:03Z)
• Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain [77.8858706250075]
  This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain. Our method performs very well with several classical face recognition test sets.
  arXiv  Detail & Related papers  (2022-07-15T07:15:36Z)
• FICGAN: Facial Identity Controllable GAN for De-identification [34.38379234653657]
  We present Facial Identity Controllable GAN (FICGAN) for generating high-quality de-identified face images with ensured privacy protection. Based on the analysis, we develop FICGAN, an autoencoder-based conditional generative model that learns to disentangle the identity attributes from non-identity attributes on a face image.
  arXiv  Detail & Related papers  (2021-10-02T07:09:27Z)
• Partial sensitivity analysis in differential privacy [58.730520380312676]
  We investigate the impact of each input feature on the individual's privacy loss. We experimentally evaluate our approach on queries over private databases. We also explore our findings in the context of neural network training on synthetic data.
  arXiv  Detail & Related papers  (2021-09-22T08:29:16Z)
• Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images [13.690485523871855]
  State-of-the-art approaches use privacy-preserving generative adversarial networks (PP-GANs) to enable reliable facial expression recognition without leaking users' identity. We show that it is possible to hide the sensitive identification data in the sanitized output images of such PP-GANs for later extraction.
  arXiv  Detail & Related papers  (2020-09-19T19:02:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.