Open Access
Diseño y captura de una base de datos para el reconocimiento de emociones minimizando sesgos
(CAEPIA, 2024) Jurío Munárriz, Aránzazu; Pascual Casas, Rubén; Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertistate Publikoa; Gobierno de Navarra / Nafarroako Gobernua
El reconocimiento de emociones a partir de expresiones faciales (FER) es un campo de investigación importante para la interacción persona-máquina. Sin embargo, los conjuntos de datos utilizados para entrenar modelos FER a menudo contienen sesgos demográficos que pueden conducir a la discriminación en el modelo final. En este trabajo, presentamos el diseño y la captura realizados para la creación de una nueva base de datos para FER, donde tratamos de minimizar los sesgos desde el propio diseño. La base de datos se ha creado utilizando diferentes métodos de captura. Para comprobar la reducción de los sesgos alcanzada, analizamos diferentes métricas de sesgo representacional y estereotípico sobre la base de datos generada y la comparamos frente a otras bases de datos estándar en la literatura de FER.
Open Access
Extensions of fuzzy sets in image processing: an overview
(EUSFLAT, 2011) Pagola Barrio, Miguel; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Fernández Fernández, Francisco Javier; Galar Idoate, Mikel; Jurío Munárriz, Aránzazu; López Molina, Carlos; Paternain Dallo, Daniel; Sanz Delgado, José Antonio; Couto, Pedro; Melo-Pinto, Pedro; Automática y Computación; Automatika eta Konputazioa
This work presents a valuable review for the interested reader of the recent Works using extensions of fuzzy sets in image processing. The chapter is divided as follows: first we recall the basics of the extensions of fuzzy sets, i.e. Type 2 fuzzy sets, interval-valued fuzzy sets and Atanassov’s intuitionistic fuzzy sets. In sequent sections we review the methods proposed for noise removal (sections 3), image enhancement (section 4), edge detection (section 5) and segmentation (section 6). There exist other image segmentation tasks such as video de-interlacing, stereo matching or object representation that are not described in this work.
Open Access
Less can be more: representational vs. stereotypical gender bias in facial expression recognition
(Springer, 2024-10-14) Domínguez Catena, Iris; Paternain Dallo, Daniel; Jurío Munárriz, Aránzazu; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Universidad Publica de Navarra / Nafarroako Unibertsitate Publikoa
Machine learning models can inherit biases from their training data, leading to discriminatory or inaccurate predictions. This is particularly concerning with the increasing use of large, unsupervised datasets for training foundational models. Traditionally, demographic biases within these datasets have not been well-understood, limiting our ability to understand how they propagate to the models themselves. To address this issue, this paper investigates the propagation of demographic biases from datasets into machine learning models. We focus on the gender demographic component, analyzing two types of bias: representational and stereotypical. For our analysis, we consider the domain of facial expression recognition (FER), a field known to exhibit biases in most popular datasets. We use Affectnet, one of the largest FER datasets, as our baseline for carefully designing and generating subsets that incorporate varying strengths of both representational and stereotypical bias. Subsequently, we train several models on these biased subsets, evaluating their performance on a common test set to assess the propagation of bias into the models¿ predictions. Our results show that representational bias has a weaker impact than expected. Models exhibit a good generalization ability even in the absence of one gender in the training dataset. Conversely, stereotypical bias has a significantly stronger impact, primarily concentrated on the biased class, although it can also influence predictions for unbiased classes. These results highlight the need for a bias analysis that differentiates between types of bias, which is crucial for the development of effective bias mitigation strategies.
Open Access
A study of different families of fusion functions for combining classifiers in the one-vs-one strategy
(Springer, 2018) Uriz Martín, Mikel Xabier; Paternain Dallo, Daniel; Jurío Munárriz, Aránzazu; Bustince Sola, Humberto; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
In this work we study the usage of different families of fusion functions for combining classifiers in a multiple classifier system of One-vs-One (OVO) classifiers. OVO is a decomposition strategy used to deal with multi-class classification problems, where the original multi-class problem is divided into as many problems as pair of classes. In a multiple classifier system, classifiers coming from different paradigms such as support vector machines, rule induction algorithms or decision trees are combined. In the literature, several works have addressed the usage of classifier selection methods for these kinds of systems, where the best classifier for each pair of classes is selected. In this work, we look at the problem from a different perspective aiming at analyzing the behavior of different families of fusion functions to combine the classifiers. In fact, a multiple classifier system of OVO classifiers can be seen as a multi-expert decision making problem. In this context, for the fusion functions depending on weights or fuzzy measures, we propose to obtain these parameters from data. Backed-up by a thorough experimental analysis we show that the fusion function to be considered is a key factor in the system. Moreover, those based on weights or fuzzy measures can allow one to better model the aggregation problem.
Open Access
A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality
(MDPI, 2025-03-09) Garcia-Pinilla, Peio; Jurío Munárriz, Aránzazu; Paternain Dallo, Daniel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Gobierno de Navarra / Nafarroako Gobernua
This paper comprehensively investigates the performance of various strategies for predicting CO2 levels in school classrooms over different time horizons by using data collected through IoT devices. We gathered Indoor Air Quality (IAQ) data from fifteen schools in Navarra, Spain between 10 January and 3 April 2022, with measurements taken at 10-min intervals. Three prediction strategies divided into seven models were trained on the data and compared using statistical tests. The study confirms that simple methodologies are effective for short-term predictions, while Machine Learning (ML)-based models perform better over longer prediction horizons. Furthermore, this study demonstrates the feasibility of using low-cost devices combined with ML models for forecasting, which can help to improve IAQ in sensitive environments such as schools.
Open Access
Aggregation functions to combine RGB color channels in stereo matching
(Optical Society of America, 2013) Galar Idoate, Mikel; Jurío Munárriz, Aránzazu; López Molina, Carlos; Sanz Delgado, José Antonio; Paternain Dallo, Daniel; Bustince Sola, Humberto; Automática y Computación; Automatika eta Konputazioa; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In this paper we present a comparison study between different aggregation functions for the combination of RGB color channels in stereo matching problem. We introduce color information from images to the stereo matching algorithm by aggregating the similarities of the RGB channels which are calculated independently. We compare the accuracy of different stereo matching algorithms and aggregation functions. We show experimentally that the best function depends on the stereo matching algorithm considered, but the dual of the geometric mean excels as the most robust aggregation.