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
Massively Annotated Datasets for Assessment of Synthetic and Real Data in Face Recognition
Pedro C. Neto, Rafael M. Mamede, Carolina Albuquerque, Tiago Gonçalves, Ana F. Sequeira
DIP-Watermark: A Double Identity Protection Method Based on Robust Adversarial Watermark
Yunming Zhang, Dengpan Ye, Caiyun Xie, Sipeng Shen, Ziyi Liu, Jiacheng Deng, Long Tang