Face Recognition
Face recognition research aims to develop accurate and robust systems for identifying individuals from their facial images. Current efforts focus on improving performance under challenging conditions (e.g., low-resolution images, occlusions), mitigating biases stemming from demographic imbalances in training data, and enhancing the explainability and security of these systems through techniques like knowledge distillation and adversarial watermarking. These advancements have significant implications for various applications, including security, law enforcement, and healthcare, while also raising important ethical considerations regarding privacy and fairness.
Papers
Rethinking Robust Representation Learning Under Fine-grained Noisy Faces
Bingqi Ma, Guanglu Song, Boxiao Liu, Yu Liu
Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing
Heng Cong, Rongyu Zhang, Jiarong He, Jin Gao
Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues
Tiago de Freitas Pereira, Dominic Schmidli, Yu Linghu, Xinyi Zhang, Sébastien Marcel, Manuel Günther
DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain
Yuxi Mi, Yuge Huang, Jiazhen Ji, Hongquan Liu, Xingkun Xu, Shouhong Ding, Shuigeng Zhou
Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain
Jiazhen Ji, Huan Wang, Yuge Huang, Jiaxiang Wu, Xingkun Xu, Shouhong Ding, ShengChuan Zhang, Liujuan Cao, Rongrong Ji
SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data
Fadi Boutros, Marco Huber, Patrick Siebke, Tim Rieber, Naser Damer
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
Youlong Ding, Xueyang Wu, Zhitao Li, Zeheng Wu, Shengqi Tan, Qian Xu, Weike Pan, Qiang Yang