Search Results for author:
Found 14 papers, 5 papers with code
Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics
We experimentally show that demographic groups highly represented in popular face databases have led to popular pre-trained deep face models presenting strong algorithmic discrimination.
Bias in Multimodal AI: Testbed for Fair Automatic Recruitment
With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious automated recruitment testbed: FairCVtest.
Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment
With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious case study focused on automated recruitment: FairCVtest.
FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment
This comparison shows that, even though FaceQgen does not surpass the best existing face quality assessment methods in terms of face recognition accuracy prediction, it achieves good enough results to demonstrate the potential of semi-supervised learning approaches for quality estimation (in particular, data-driven learning based on a single high quality image per subject), having the capacity to improve its performance in the future with adequate refinement of the model and the significant advantage over competing methods of not needing quality labels for its development.
InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics
We analyze how bias affects deep learning processes through a toy example using the MNIST database and a case study in gender detection from face images.
SensitiveLoss: Improving Accuracy and Fairness of Face Representations with Discrimination-Aware Deep Learning
We propose a discrimination-aware learning method to improve both accuracy and fairness of biased face recognition algorithms.
FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment
With the aim of studying how current multimodal AI algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, this demonstrator experiments over an automated recruitment testbed based on Curriculum Vitae: FairCVtest.
Facial Expressions as a Vulnerability in Face Recognition
This work explores facial expression bias as a security vulnerability of face recognition systems.
SetMargin Loss applied to Deep Keystroke Biometrics with Circle Packing Interpretation
This work presents a new deep learning approach for keystroke biometrics based on a novel Distance Metric Learning method (DML).
IFBiD: Inference-Free Bias Detection
This paper is the first to explore an automatic way to detect bias in deep convolutional neural networks by simply looking at their weights.
OTB-morph: One-Time Biometrics via Morphing applied to Face Templates
Cancelable biometrics refers to a group of techniques in which the biometric inputs are transformed intentionally using a key before processing or storage.
OTB-morph: One-Time Biometrics via Morphing
Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems.
Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma
This risk assessment should address, among others, the detection and mitigation of bias in AI.
Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs
The analysis of public affairs documents is crucial for citizens as it promotes transparency, accountability, and informed decision-making.
