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Found 21 papers, 11 papers with code
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-onGoing challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024.
How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recognition
Leveraging the capabilities of Knowledge Distillation (KD) strategies, we devise a strategy to fight the recent retraction of face recognition datasets.
MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition
Our strategy learns a project of these four teachers into a common space and distill that information to a student network.
Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition
This study investigates the effects of occlusions on the fairness of face recognition systems, particularly focusing on demographic biases.
Massively Annotated Datasets for Assessment of Synthetic and Real Data in Face Recognition
Additionally, we further inspect the differences between real and synthetic datasets on the attribute set.
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.
Model Compression Techniques in Biometrics Applications: A Survey
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity.
Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition
We investigate the overall performance, the performance on each ethnicity subgroup and the racial bias of a State-of-the-Art quantization approach when used with synthetic and real data.
Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection
Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown.
Ranked #1 on
Face Morphing Attack Detection
on FRLL-Morphs
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides
On the internal dataset, the method shows an accuracy of 93. 44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0. 996.
PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition
In the context of biometrics, matching confidence refers to the confidence that a given matching decision is correct.
Ranked #1 on
Face Recognition
on MORPH
Causality-Inspired Taxonomy for Explainable Artificial Intelligence
As two sides of the same coin, causality and explainable artificial intelligence (xAI) were initially proposed and developed with different goals.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems.
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries.
OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions
This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022).
Myope Models -- Are face presentation attack detection models short-sighted?
Presentation attacks are recurrent threats to biometric systems, where impostors attempt to bypass these systems.
Ranked #1 on
Face Presentation Attack Detection
on ROSE-YOUTU
FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks.
My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition
The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS).
CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples.
Ranked #1 on
Colorectal Polyps Characterization
on CRC
MFR 2021: Masked Face Recognition Competition
These teams successfully submitted 18 valid solutions.
Deep Learning Based Analysis of Prostate Cancer from MP-MRI
While for segmentation two models, two input sizes and data augmentations were experimented.
