Search Results for author:
Found 8 papers, 6 papers with code
Tackling Face Verification Edge Cases: In-Depth Analysis and Human-Machine Fusion Approach
Finally, we demonstrate that combining machine and human decisions can further improve the performance of state-of-the-art face verification systems on various benchmark datasets.
Explainable Model-Agnostic Similarity and Confidence in Face Verification
This work focuses on explanations for face recognition systems, vital for developers and operators.
Octuplet Loss: Make Face Recognition Robust to Image Resolution
To address this problem, we propose a novel combination of the popular triplet loss to improve robustness against image resolution via fine-tuning of existing face recognition models.
Ranked #1 on
Face Recognition
on XQLFW
Face Morphing: Fooling a Face Recognition System Is Simple!
State-of-the-art face recognition (FR) approaches have shown remarkable results in predicting whether two faces belong to the same identity, yielding accuracies between 92% and 100% depending on the difficulty of the protocol.
Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments
Real-world face recognition applications often deal with suboptimal image quality or resolution due to different capturing conditions such as various subject-to-camera distances, poor camera settings, or motion blur.
Susceptibility to Image Resolution in Face Recognition and Trainings Strategies
In this work, we first analyze the impact of image resolutions on face verification performance with a state-of-the-art face recognition model.
Attention-based Partial Face Recognition
In this paper, we propose a novel approach to partial face recognition capable of recognizing faces with different occluded areas.
A Multi-Task Comparator Framework for Kinship Verification
Approaches for kinship verification often rely on cosine distances between face identification features.
