Jul 2026· IEEE Transactions on Pattern Analysis and Machine Intelligence· Vol PP· 0 citations· 224 references
MedicineComputer Science
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.
A survey of demographic attribute estimation from periocular images, covering publicly available datasets, methodological trends from handcrafted descriptors to deep learning architectures, and the state of the art in gender, age, and ethnicity prediction is provided.
F. Alonso-Fernandez, Kevin Hernandez-Diaz, J. Bigun· 0 citations
The human hand serves as a vital biometric modality, with the palm, fingers, and knuckles commonly employed for identity recognition across a range of applications. Despite the abundance of sensitive identity cues present in hand imagery, comprehensive privacy protection, particularly full-hand de-identification, remains underexplored. To address this gap, we introduce a self-semantic-structural-guided de-identification framework that operates directly on a single hand image, eliminating the need for training or optimization. Our method builds upon a pre-trained diffusion-based inpainting model, enhanced with IP-Adapter and ControlNet to incorporate semantic and structural cues. To mitigate identity leakage, we devise a selective semantic injection strategy and a depth-based structural de-identification strategy for each branch. Additionally, to maintain background integrity, we implement a self-adaptive hand-blending technique that seamlessly integrates the de-identified hand into the original scene. Extensive evaluations across multiple datasets and recognition systems validate the method’s effectiveness in preserving image quality, usability, and de-identification fidelity.
Licheng Yan, Yifan Lyu, Weiliang Huang et al.· IEEE Transactions on Informa...· 0 citations
Through comprehensive evaluations and analysis, it is shown that LFD effectively captures shared features and artifacts across different lensless imaging devices, making it a valuable dataset for advancing lensless face recognition.
Junho Kim, S. S. Khan, Sara Wan et al.· 0 citations
A detection framework is proposed that extracts per-video rPPG wave- forms via RhythmFormer and trains a suite of lightweight classifiers to distinguish real from synthesized physiologi- cal signals and shows that detec- tion difficulty is strongly method-dependent.
Missing person identification using surveillance imagery remains a challenging problem due to adverse visual conditions, limited availability of reference images, and strict privacy constraints surrounding real-world data. This paper presents a hybrid deep-learning framework for missing person identification that combines YOLO-based person detection with deep face embedding models, specifically FaceNet and ArcFace. To address ethical and privacy limitations, a manually curated composite synthetic dataset is constructed by combining publicly available crowd and in-the-wild face datasets with additional curated images to realistically emulate CCTV conditions, including low illumination, occlusions, visually similar individuals, accessories, and clothing-matched decoys, while restricting each identity to only three to four reference images. The proposed system is evaluated across five YOLO variants (v8–v12) and a wide range of cosine similarity thresholds to analyze detection sensitivity, false positive behavior, and overall identification robustness. Extensive experiments conducted on 780 group images demonstrate that the YOLO–ArcFace pipeline achieves superior performance, reaching a peak identification accuracy of 97.50% with near-zero false positives, while the YOLO–FaceNet pipeline attains a maximum accuracy of 95.42% at an optimized operating threshold but exhibits higher susceptibility to false matches. Threshold–accuracy analysis and operating point comparison reveal that ArcFace embeddings provide stronger inter-class separation and greater stability under surveillance-specific distortions. The results highlight the importance of discriminative embedding models in safety-critical identification tasks and demonstrate that reliable missing person detection is achievable even under constrained and privacy-preserving settings, with future extensions proposed toward continuous video-based tracking using multi-object tracking algorithms.
G. S. Rakshika, Babyrani Waikhom, U. Muthaiah· International Conference Com...· 0 citations
A disentanglement method based on Mutual Information Minimization is introduced to minimize statistical dependence between modality-shared and modality-specific features from a probability distribution perspective and a Local Discriminative Attention Module is designed to adaptively focus on highly informative body parts such as head-shoulder ratio and torso patterns.
Xiaokai Liu, Fangqing Zhou, Qian Song et al.· Advances in Engineering Tech...· 0 citations