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
Facial recognition technology and human raters can predict political orientation from images of expressionless faces even when controlling for demographics and self-presentation
Michal Kosinski, Poruz Khambatta, Yilun Wang
Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition
Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu
Improvement of Color Image Analysis Using a New Hybrid Face Recognition Algorithm based on Discrete Wavelets and Chebyshev Polynomials
Hassan Mohamed Muhi-Aldeen, Maha Ammar Mustafa, Asma A. Abdulrahman, Jabbar Abed Eleiwy, Fouad S. Tahir, Yurii Khlaponin
Watch Out for the Confusing Faces: Detecting Face Swapping with the Probability Distribution of Face Identification Models
Yuxuan Duan, Xuhong Zhang, Chuer Yu, Zonghui Wang, Shouling Ji, Wenzhi Chen
DaliID: Distortion-Adaptive Learned Invariance for Identification Models
Wes Robbins, Gabriel Bertocco, Terrance E. Boult
Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New Baseline
Xu Ling, Yichen Lu, Wenqi Xu, Weihong Deng, Yingjie Zhang, Xingchen Cui, Hongzhi Shi, Dongchao Wen
Sketch Less Face Image Retrieval: A New Challenge
Dawei Dai, Yutang Li, Liang Wang, Shiyu Fu, Shuyin Xia, Guoyin Wang