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Found 6 papers, 3 papers with code
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces
Finally, we assessed the effectiveness of data augmentation approaches on synthetic and authentic data, with the same goal in mind.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
(Un)fair Exposure in Deep Face Rankings at a Distance
Law enforcement regularly faces the challenge of ranking suspects from their facial images.
The More Secure, The Less Equally Usable: Gender and Ethnicity (Un)fairness of Deep Face Recognition along Security Thresholds
Commonly, the recognition threshold of a face recognition system is adjusted based on the degree of security for the considered use case.
Explaining Bias in Deep Face Recognition via Image Characteristics
In this paper, we propose a novel explanatory framework aimed to provide a better understanding of how face recognition models perform as the underlying data characteristics (protected attributes: gender, ethnicity, age; non-protected attributes: facial hair, makeup, accessories, face orientation and occlusion, image distortion, emotions) on which they are tested change.
