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
Deep Network Pruning: A Comparative Study on CNNs in Face Recognition
Fernando Alonso-Fernandez, Kevin Hernandez-Diaz, Jose Maria Buades Rubio, Prayag Tiwari, Josef Bigun
Exploring Thermography Technology: A Comprehensive Facial Dataset for Face Detection, Recognition, and Emotion
Mohamed Fawzi Abdelshafie Abuhussein, Ashraf Darwish, Aboul Ella Hassanien
Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models
Fengfan Zhou, Qianyu Zhou, Xiangtai Li, Xuequan Lu, Lizhuang Ma, Hefei Ling
Masked Face Recognition with Generative-to-Discriminative Representations
Shiming Ge, Weijia Guo, Chenyu Li, Junzheng Zhang, Yong Li, Dan Zeng
Hypergraph Laplacian Eigenmaps and Face Recognition Problems
Loc Hoang Tran
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