papers

Publications (19)

cs.CV2022

IFBiD: Inference-Free Bias Detection

Ignacio Serna, Daniel DeAlcala, Aythami Morales +2

This paper is the first to explore an automatic way to detect bias in deep convolutional neural networks by simply looking at their weights. Furthermore, it is also a step towards…

cs.LG2023

Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment

Alejandro Peña, Ignacio Serna, Aythami Morales +5

The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new s…

cs.CV2022

FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment

Javier Hernandez-Ortega, Julian Fierrez, Ignacio Serna +1

In this paper we develop FaceQgen, a No-Reference Quality Assessment approach for face images based on a Generative Adversarial Network that generates a scalar quality measure rela…

cs.CV2026

Discovering Intersectional Bias via Directional Alignment in Face Recognition Embeddings

Ignacio Serna

Modern face recognition models embed identities on a unit hypersphere, where identity variation forms tight clusters. Conversely, shared semantic attributes can often be effectivel…

cs.LG2023

Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma

Daniel DeAlcala, Ignacio Serna, Aythami Morales +2

The new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the…

cs.AI2023

Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs

Alejandro Peña, Aythami Morales, Julian Fierrez +5

The analysis of public affairs documents is crucial for citizens as it promotes transparency, accountability, and informed decision-making. It allows citizens to understand governm…

cs.CV2021

SetMargin Loss applied to Deep Keystroke Biometrics with Circle Packing Interpretation

Aythami Morales, Julian Fierrez, Alejandro Acien +2

This work presents a new deep learning approach for keystroke biometrics based on a novel Distance Metric Learning method (DML). DML maps input data into a learned representation s…

cs.CV2020

FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment

Alejandro Peña, Ignacio Serna, Aythami Morales +1

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 de…

cs.AI2024

Alien Recombination: Exploring Concept Blends Beyond Human Cognitive Availability in Visual Art

Alejandro Hernandez, Levin Brinkmann, Ignacio Serna +6

While AI models have demonstrated remarkable capabilities in constrained domains like game strategy, their potential for genuine creativity in open-ended domains like art remains d…

cs.CV2019

Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics

Ignacio Serna, Aythami Morales, Julian Fierrez +3

The most popular face recognition benchmarks assume a distribution of subjects without much attention to their demographic attributes. In this work, we perform a comprehensive disc…

cs.LG2023

OTB-morph: One-Time Biometrics via Morphing

Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez +2

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…

cs.CV2021

Facial Expressions as a Vulnerability in Face Recognition

Alejandro Peña, Ignacio Serna, Aythami Morales +2

This work explores facial expression bias as a security vulnerability of face recognition systems. Despite the great performance achieved by state-of-the-art face recognition syste…

cs.CV2025

A Rapid Test for Accuracy and Bias of Face Recognition Technology

Manuel Knott, Ignacio Serna, Ethan Mann +1

Measuring the accuracy of face recognition (FR) systems is essential for improving performance and ensuring responsible use. Accuracy is typically estimated using large annotated d…

cs.CV2020

Bias in Multimodal AI: Testbed for Fair Automatic Recruitment

Alejandro Peña, Ignacio Serna, Aythami Morales +1

The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new s…

cs.CV2021

OTB-morph: One-Time Biometrics via Morphing applied to Face Templates

Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez +2

Cancelable biometrics refers to a group of techniques in which the biometric inputs are transformed intentionally using a key before processing or storage. This transformation is r…

cs.AI2025

Cultural Alien Sampler: Open-ended art generation balancing originality and coherence

Alejandro H. Artiles, Hiromu Yakura, Levin Brinkmann +6

In open-ended domains like art, autonomous agents must generate ideas that are both original and internally coherent, yet current Large Language Models (LLMs) either default to fam…

cs.CV2020

SensitiveLoss: Improving Accuracy and Fairness of Face Representations with Discrimination-Aware Deep Learning

Ignacio Serna, Aythami Morales, Julian Fierrez +3

We propose a discrimination-aware learning method to improve both accuracy and fairness of biased face recognition algorithms. The most popular face recognition benchmarks assume a…

cs.CV2026

Unraveling Machine Behavior by Multi-Level Bias Analysis and Detection: Methodology and Application to Computer Vision

Ignacio Serna, Aythami Morales, Julian Fierrez

This study investigates the presence and propagation of bias within Neural Networks through a comprehensive multi-level analysis spanning the learned latent space, layer activation…

cs.CV2020

InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics

Ignacio Serna, Alejandro Peña, Aythami Morales +1

This work explores the biases in learning processes based on deep neural network architectures. We analyze how bias affects deep learning processes through a toy example using the…