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
Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
Rafael Henrique Vareto, Manuel Günther, William Robson Schwartz
Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition
Pedro C. Neto, Eduarda Caldeira, Jaime S. Cardoso, Ana F. Sequeira
Open-set Face Recognition using Ensembles trained on Clustered Data
Rafael Henrique Vareto, William Robson Schwartz
AAFACE: Attribute-aware Attentional Network for Face Recognition
Niloufar Alipour Talemi, Hossein Kashiani, Sahar Rahimi Malakshan, Mohammad Saeed Ebrahimi Saadabadi, Nima Najafzadeh, Mohammad Akyash, Nasser M. Nasrabadi