AA
Andrea Atzori
Fraunhofer Institute for Computer Graphics Research
Research areas: Computer Science · Computer Vision and Pattern Recognition · Face recognition and analysis · Face and Expression Recognition · Biometric Identification and Security
Employment
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Fraunhofer Institute for Computer Graphics Research Postdoctoral Researcher2025 - Present
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (28)
- ATTN-FIQA: Interpretable Attention-based Face Image Quality Assessment with Vision Transformers Save
- EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment Save
- PreFIQs: Face Image Quality Is What Survives Pruning Save
- Second FRCSyn-onGoing: Winning solutions and post-challenge analysis to improve face recognition with synthetic data Save
- The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition Save
- ViT-FIQA: Assessing Face Image Quality using Vision Transformers Save
- ViT-FIQA: Assessing Face Image Quality using Vision Transformers Save
- ViT-FIQA: Assessing Face Image Quality using Vision Transformers Save
- FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data Save
- FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems Save
- If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces Save
- If It’s Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces Save
- Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data Save
- Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data Save
- Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data Save
- Second Frcsyn-Ongoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data Save
- The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition Save
- Demographic Bias in Low-Resolution Deep Face Recognition in the Wild Save
- (Un)fair Exposure in Deep Face Rankings at a Distance Save
- (Un)fair Exposure in Deep Face Rankings at a Distance Save