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
How to Boost Face Recognition with StyleGAN?
Artem Sevastopolsky, Yury Malkov, Nikita Durasov, Luisa Verdoliva, Matthias Nießner
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
Samuel Dooley, Rhea Sanjay Sukthanker, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum
Face Pasting Attack
Niklas Bunzel, Lukas Graner
Weakly Supervised Face Naming with Symmetry-Enhanced Contrastive Loss
Tingyu Qu, Tinne Tuytelaars, Marie-Francine Moens
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark
Zhizhong Huang, Junping Zhang, Hongming Shan
DigiFace-1M: 1 Million Digital Face Images for Face Recognition
Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, Jingjing Shen
InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition
Meng Sang, Jiaxuan Chen, Mengzhen Li, Pan Tan, Anning Pan, Shan Zhao, Yang Yang