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
Distributionally Generative Augmentation for Fair Facial Attribute Classification
Fengda Zhang, Qianpei He, Kun Kuang, Jiashuo Liu, Long Chen, Chao Wu, Jun Xiao, Hanwang Zhang
Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis
Zijian Chen, Mei Wang, Weihong Deng, Hongzhi Shi, Dongchao Wen, Yingjie Zhang, Xingchen Cui, Jian Zhao
Balancing Act: Distribution-Guided Debiasing in Diffusion Models
Rishubh Parihar, Abhijnya Bhat, Abhipsa Basu, Saswat Mallick, Jogendra Nath Kundu, R. Venkatesh Babu
Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit Precision
William Gazali, Jocelyn Michelle Kho, Joshua Santoso, Williem