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
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