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
Adaptive Template Enhancement for Improved Person Recognition using Small Datasets
Su Yang, Sanaul Hoque, Farzin Deravi
Local Directional Gradient Pattern: A Local Descriptor for Face Recognition
Soumendu Chakraborty, Satish Kumar Singh, Pavan Chakraborty
Local Quadruple Pattern: A Novel Descriptor for Facial Image Recognition and Retrieval
Soumendu Chakraborty, Satish Kumar Singh, Pavan Chakraborty
Centre Symmetric Quadruple Pattern: A Novel Descriptor for Facial Image Recognition and Retrieval
Soumendu Chakraborty, Satish Kumar Singh, Pavan Chakraborty
Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval
Soumendu Chakraborty, Satish Kumar Singh, Pavan Chakraborty
R-Theta Local Neighborhood Pattern for Unconstrained Facial Image Recognition and Retrieval
Soumendu Chakraborty, Satish Kumar Singh, Pavan Chakraborty
Biometrics in the Time of Pandemic: 40% Masked Face Recognition Degradation can be Reduced to 2%
Leonardo Queiroz, Kenneth Lai, Svetlana Yanushkevich, Vlad Shmerko