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
semantic neural model approach for face recognition from sketch
Chandana Navuluri, Sandhya Jukanti, Raghupathi Reddy Allapuram
Synthetic Data for Face Recognition: Current State and Future Prospects
Fadi Boutros, Vitomir Struc, Julian Fierrez, Naser Damer
Racial Bias within Face Recognition: A Survey
Seyma Yucer, Furkan Tektas, Noura Al Moubayed, Toby P. Breckon