Face De-Identification: A Domain-Centric Survey from Capture to Processing

• URL: http://arxiv.org/abs/2607.25926v1
• Date: Tue, 28 Jul 2026 16:15:12 GMT
• Title: Face De-Identification: A Domain-Centric Survey from Capture to Processing
• Abstract summary: Face de-identification (De-ID) aims to remove or conceal personally identifiable facial features in images or videos.<n>Recent advances extend face De-ID beyond post-processing by integrating privacy mechanisms directly into sensors during image acquisition.
• Score: 24.624419105765867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face de-identification (De-ID) aims to remove or conceal personally identifiable facial features in images or videos to prevent identity recognition while preserving utility for downstream tasks. With the rising emphasis on data privacy and responsible AI, face De-ID has emerged as an active research area spanning computer vision and privacy-preserving communities. Early approaches, and many contemporary ones, operate in the digital domain by modifying pixel-level or appearance-level features through post-capture processing. Recent advances extend face De-ID beyond post-processing by integrating privacy mechanisms directly into sensors during image acquisition, bridging sensing systems and downstream vision algorithms. In parallel, physical-domain methods explore wearable accessories and materials that conceal identity information in real-world environments prior to capture. In this survey, we present the first unified overview that spans the full data acquisition pipeline, encompassing the physical, sensor, and digital domains. Through this domain-centric lens, we systematically analyze current methodologies, technical progress, and the distinct challenges inherent to each stage. We then review and organize existing evaluation protocols, examining current practices and highlighting the critical need for standardized, comprehensive benchmarks. Finally, we identify key open problems and outline emerging research directions to guide future work in this rapidly evolving field. To support ongoing research, we maintain a project page that organizes relevant literature with collected datasets and open source code: https://github.com/CV-AC/Awesome-FaceDe-ID.

Related papers

• Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook [85.43403500874889]
  Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI)<n>Recent advancements in RAG for embodied AI, with a particular focus on applications in planning, task execution, multimodal perception, interaction, and specialized domains.
  arXiv  Detail & Related papers  (2025-03-23T10:33:28Z)
• Survey on Hand Gesture Recognition from Visual Input [1.0431392282476837]
  Hand gesture recognition has become an important research area, driven by the growing demand for human-computer interaction.<n>There are few surveys that comprehensively cover recent research developments, available solutions, and benchmark datasets.<n>This survey addresses this gap by examining the latest advancements in hand gesture and 3D hand pose recognition from various types of camera input data.
  arXiv  Detail & Related papers  (2025-01-21T09:23:22Z)
• Face De-identification: State-of-the-art Methods and Comparative Studies [32.333766763819796]
  Face de-identification is regarded as an effective means to protect the privacy of facial images. We provide a review of state-of-the-art face de-identification methods, categorized into three levels: pixel-level, representation-level, and semantic-level techniques.
  arXiv  Detail & Related papers  (2024-11-15T01:00:00Z)
• Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
  We propose a hardware-level face de-identification method to solve this vulnerability. We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
  arXiv  Detail & Related papers  (2024-03-31T19:28:04Z)
• Deepfake Generation and Detection: A Benchmark and Survey [134.19054491600832]
  Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions. This survey comprehensively reviews the latest developments in deepfake generation and detection. We focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing.
  arXiv  Detail & Related papers  (2024-03-26T17:12:34Z)
• RID-TWIN: An end-to-end pipeline for automatic face de-identification in videos [2.7569134765233536]
  RID-Twin is a pipeline that decouples identity from motion to perform automatic face de-identification in videos. We evaluate the performance of our methodology on the widely employed VoxCeleb2 dataset.
  arXiv  Detail & Related papers  (2024-03-15T06:59:21Z)
• Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis [16.394031759681678]
  We motivate and introduce the Pedestrian dataset De-Identification task. PDI evaluates the degree of de-identification and downstream task training performance for a given de-identification method. We show how our data is able to narrow the synthetic-to-real performance gap in a privacy-conscious manner.
  arXiv  Detail & Related papers  (2023-06-20T17:39:24Z)
• Disguise without Disruption: Utility-Preserving Face De-Identification [40.484745636190034]
  We introduce Disguise, a novel algorithm that seamlessly de-identifies facial images while ensuring the usability of the modified data. Our method involves extracting and substituting depicted identities with synthetic ones, generated using variational mechanisms to maximize obfuscation and non-invertibility. We extensively evaluate our method using multiple datasets, demonstrating a higher de-identification rate and superior consistency compared to prior approaches in various downstream tasks.
  arXiv  Detail & Related papers  (2023-03-23T13:50:46Z)
• A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
  The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
  arXiv  Detail & Related papers  (2022-11-07T17:20:39Z)
• Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
  Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications. Learning highly accurate models relies on the availability of datasets with a large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
  arXiv  Detail & Related papers  (2021-05-27T23:34:06Z)
• The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances [56.432660252331495]
  Face recognition is one of the most popular and long-standing topics in computer vision. Deep face recognition has made remarkable progress and been widely used in many real-world applications. In this survey article, we present a comprehensive review about the recent advance of each element.
  arXiv  Detail & Related papers  (2020-09-28T13:02:17Z)

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.