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Updated: Aug 5, 2026
09:49
Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
Published on: December 24, 2015
Face De-Identification: A Domain-Centric Survey from Capture to Processing
Summary
Face de-identification (De-ID) methods protect privacy by concealing facial features. This survey unifies physical, sensor, and digital approaches, analyzing challenges and guiding future research in responsible AI.
Area of Science:
- Computer Vision
- Privacy-Preserving Technologies
- Artificial Intelligence
Background:
- Face de-identification (De-ID) is crucial for data privacy and responsible AI.
- Existing methods primarily focus on post-capture digital processing.
- Emerging techniques integrate privacy into the image acquisition process.
Purpose of the Study:
- To provide a unified overview of face De-ID methods across the full data acquisition pipeline.
- To systematically analyze methodologies, progress, and challenges in physical, sensor, and digital domains.
- To identify open problems and guide future research directions.
Main Methods:
- Comprehensive survey of face De-ID literature.
- Analysis categorized by data acquisition domain: physical, sensor, and digital.
- Review of current evaluation protocols and benchmarking.
Main Results:
- Categorization of face De-ID techniques into physical, sensor, and digital domains.
- Systematic analysis of methodologies and challenges within each domain.
- Identification of gaps in current evaluation protocols and benchmarks.
Conclusions:
- A unified, domain-centric perspective is needed for face De-ID research.
- Standardized benchmarks are critical for evaluating and advancing face De-ID technologies.
- Future work should address open problems in integrating privacy across the acquisition pipeline.
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