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Synthetic data for the Mitigation of Demographic Biases in Face Recognition

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arXiv 2024年

作者： Melzi, Pietro Rathgeb, Christian Tolosana, Ruben Vera-Rodriguez, Ruben Morales, Aythami Lawatsch, Dominik Domin, Florian Schaubert, Maxim Biometrics and Data Pattern Analytics Laboratory Universidad Autonoma de Madrid Spain secunet Security Networks AG Essen Germany Hochschule Darmstadt Germany

This study investigates the possibility of mitigating the demographic biases that affect face recognition technologies through the use of synthetic data. Demographic biases have the potential to impact individuals from specific demographic groups, and can be identified by observing disparate performance of face recognition systems across demographic groups. They primarily arise from the unequal representations of demographic groups in the training data. In recent times, synthetic data have emerged as a solution to some problems that affect face recognition systems. In particular, during the generation process it is possible to specify the desired demographic and facial attributes of images, in order to control the demographic distribution of the synthesized dataset, and fairly represent the different demographic groups. We propose to fine-tune with synthetic data existing face recognition systems that present some demographic biases. We use synthetic datasets generated with GANDiffFace, a novel framework able to synthesize datasets for face recognition with controllable demographic distribution and realistic intra-class variations. We consider multiple datasets representing different demographic groups for training and evaluation. Also, we fine-tune different face recognition systems, and evaluate their demographic fairness with different metrics. Our results support the proposed approach and the use of synthetic data to mitigate demographic biases in face recognition. © 2024, CC BY-NC-ND.

关键词： Face recognition

mEBAL2 database and Benchmark: Image-based Multispectral Eyeblink Detection

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arXiv 2023年

作者： Daza, Roberto Morales, Aythami Fierrez, Julian Tolosana, Ruben Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics Laboratory Universidad Autonoma de Madrid Calle Francisco Tomas y Valiente 11 Campus de Cantoblanco Madrid28049 Spain

This work introduces a new multispectral database and framework to train and evaluate eyeblink detection in RGB and Near-Infrared (NIR). Our contributed dataset (mEBAL2, multimodal EyeBlink and Attention Level estimation, Version 2) is the largest existing eyeblink database, representing a great opportunity to improve data-driven multispectral approaches for blink detection and related applications (e.g., attention level estimation). mEBAL2 includes 21,100 image sequences from 180 different students (more than 2 million labeled images in total) while conducting a number of e-learning tasks of varying difficulty or taking a real course on HTML initiation through the edX MOOC platform. mEBAL2 uses multiple sensors, including two Near-Infrared (NIR) and one RGB camera to capture facial gestures during the execution of the tasks, as well as an Electroencephalogram (EEG) band to get the cognitive activity of the user and blinking events. Furthermore, this work proposes 3 data-driven approaches as benchmarks for blink detection on mEBAL2, where the architecture based on Convolutional Long Short-Term Memory (ConvLSTM) achieved performances of up to 99%. The experiments explored whether combining RGB and NIR spectrum data improves blink detection in training and architectures that merge both types of data. Experiments showed that the NIR spectrum enhances results, even when only RGB images are available during inference. Finally, the generalization capacity of the proposed eyeblink detectors, along with state-of-the-art eyeblink detection implementations, is validated in wilder and more challenging environments like the HUST-LEBW dataset to show the usefulness of mEBAL2 to train a new generation of data-driven approaches for eyeblink detection. Copyright © 2023, The Authors. All rights reserved.

关键词： database systems

Facial expressions as a vulnerability in face recognition

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arXiv 2020年

作者： Peña, Alejandro Morales, Aythami Serna, Ignacio Fierrez, Julian Lapedriza, Agata Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Computer Science Dpt. eHealth Center Universitat Oberta de Catalunya Spain

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 systems, the algorithms are still sensitive to a large range of covariates. We present a comprehensive analysis of how facial expression bias impacts the performance of face recognition technologies. Our study analyzes: i) facial expression biases in the most popular face recognition databases;and ii) the impact of facial expression in face recognition performances. Our experimental framework includes two face detectors, three face recognition models, and three different databases. Our results demonstrate a huge facial expression bias in the most widely used databases, as well as a related impact of face expression in the performance of state-of-the-art algorithms. This work opens the door to new research lines focused on mitigating the observed vulnerability. © 2020, CC BY-NC-ND.

关键词： Face recognition

FaceGenderID: Exploiting Gender Information in DCNNs Face Recognition Systems

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FaceGenderID: Exploiting Gender Information in DCNNs Face Re...

IEEE Computer Society Conference on Computer Vision and pattern Recognition Workshops (CVPRW)

作者： Ruben Vera-Rodriguez Marta Blazquez Aythami Morales Ester Gonzalez-Sosa João C. Neves Hugo Proença Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Madrid Spain Nokia Bell Labs Madrid Spain University of Beira Interior Covilha Portugal

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

This paper addresses the effect of gender as a covariate in face verification systems. Even though pre-trained models based on Deep Convolutional Neural Networks (DCNNs), such as VGG-Face or ResNet-50, achieve very high performance, they are trained on very large datasets comprising millions of images, which have biases regarding demographic aspects like the gender and the ethnicity among others. In this work, we first analyse the separate performance of these state-of-the-art models for males and females. We observe a gap between face verification performances obtained by both gender classes. These results suggest that features obtained by biased models are affected by the gender covariate. We propose a gender-dependent training approach to improve the feature representation for both genders, and develop both: i) gender specific DCNNs models, and ii) a gender balanced DCNNs model. Our results show significant and consistent improvements in face verification performance for both genders, individually and in general with our proposed approach. Finally, we announce the availability (at GitHub) of the FaceGenderID DCNNs models proposed in this work, which can support further experiments on this topic.

关键词： Face Face recognition Training databases Estimation data models

ChildCI Framework: Analysis of Motor and Cognitive Development in Children-Computer Interaction for Age Detection

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arXiv 2022年

作者： Ruiz-Garcia, Juan Carlos Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Herreros-Rodriguez, Jaime The Biometrics and Data Pattern Analytics - BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain The Hospital Universitario Infanta Leonor Madrid28031 Spain

This article presents a comprehensive analysis of the different tests proposed in the recent ChildCI framework, proving its potential for generating a better understanding of children's neuromotor and cognitive development along time, as well as their possible application in other research areas such as e-Health and e-Learning. In particular, we propose a set of over 100 global features related to motor and cognitive aspects of the children interaction with mobile devices, some of them collected and adapted from the literature. Furthermore, we analyse the robustness and discriminative power of the proposed feature set including experimental results for the task of children age group detection based on their motor and cognitive behaviours. Two different scenarios are considered in this study: i) single-test scenario, and ii) multiple-test scenario. Results over 93% accuracy are achieved using the publicly available ChildCIdb_v1 database (over 400 children from 18 months to 8 years old), proving the high correlation of children's age with the way they interact with mobile devices. © 2022, CC BY-NC-ND.

关键词：

Longitudinal Analysis and Quantitative Assessment of Child Development through Mobile Interaction

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arXiv 2024年

作者： Ruiz-Garcia, Juan Carlos Tolosana, Ruben Vera-Rodriguez, Ruben Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Herreros-Rodriguez, Jaime The Biometrics and Data Pattern Analytics - BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain The Hospital Universitario Infanta Leonor Madrid28031 Spain

This article provides a comprehensive overview of recent research in the area of Child-Computer Interaction (CCI). The main contributions of the present article are two-fold. First, we present a novel longitudinal CCI database named ChildCIdbLong1, which comprises over 600 children aged 18 months to 8 years old, acquired continuously over 4 academic years (2019-2023). As a result, ChildCIdbLong comprises over 12K test acquisitions over a tablet device. Different tests are considered in ChildCIdbLong, requiring different touch and stylus gestures, enabling the evaluation of praxical and cognitive skills such as attentional, visuo-spatial, and executive, among others. In addition to the ChildCIdbLong database, we propose a novel quantitative metric called Test Quality (Q), designed to measure the motor and cognitive development of children through their interaction with a tablet device. In order to provide a better comprehension of the proposed Q metric, popular percentile-based growth representations are introduced for each test, providing a two-dimensional space to compare children's development with respect to the typical age skills of the population. The results achieved in the present article highlight the potential of the novel ChildCIdbLong database in conjunction with the proposed Q metric to measure the motor and cognitive development of children as they grow up. The proposed framework could be very useful as an automatic tool to support child experts (e.g., paediatricians, educators, or neurologists) for early detection of potential physical/cognitive impairments during children's development. © 2024, CC BY-NC-ND.

关键词： Electronic health record

Child-computer interaction: Recent works, new dataset, and age detection

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arXiv 2021年

作者： Tolosana, Ruben Ruiz-Garcia, Juan Carlos Vera-Rodriguez, Ruben Herreros-Rodriguez, Jaime Romero-Tapiador, Sergio Morales, Aythami Fierrez, Julian Biometrics and Data Pattern Analytics - BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain Hospital Universitario Infanta Leonor Madrid28031 Spain

We overview recent research in Child-Computer Interaction and describe our framework ChildCI intended for: i) generating a better understanding of the cognitive and neuromotor development of children while interacting with mobile devices, and ii) enabling new applications in e-learning and e-health, among others. Our framework includes a new mobile application, specific data acquisition protocols, and a first release of the ChildCI dataset (ChildCIdb v1), which is planned to be extended yearly to enable longitudinal studies. In our framework children interact with a tablet device, using both a pen stylus and the finger, performing different tasks that require different levels of neuromotor and cognitive skills. ChildCIdb comprises more than 400 children from 18 months to 8 years old, considering therefore the first three development stages of the Piaget's theory. In addition, and as a demonstration of the potential of the ChildCI framework, we include experimental results for one of the many applications enabled by ChildCIdb: children age detection based on device interaction. Different machine learning approaches are evaluated, proposing a new set of 34 global features to automatically detect age groups, achieving accuracy results over 90% and interesting findings in terms of the type of features more useful for this task. © 2021, CC BY-NC-ND.

关键词： Computer vision

GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection

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arXiv 2019年

作者： Neves, João C. Tolosana, Ruben Vera-Rodriguez, Ruben Lopes, Vasco Proença, Hugo Fierrez, Julian NOVA-LINCS Portugal The Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Spain University of Beira Interior Portugal IT - Instituto de Telecomunicações Portugal

The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse. Such concerns have fostered the research on manipulation detection methods that, contrary to humans, have already achieved astonishing results in various scenarios. In this study, we focus on the synthesis of entire facial images, which is a specific type of facial manipulation. The main contributions of this study are four-fold: i) a novel strategy to remove GAN "fingerprints" from synthetic fake images based on autoencoders is described, in order to spoof facial manipulation detection systems while keeping the visual quality of the resulting images;ii) an in-depth analysis of the recent literature in facial manipulation detection;iii) a complete experimental assessment of this type of facial manipulation, considering the state-of-the-art fake detection systems (based on holistic deep networks, steganalysis, and local artifacts), remarking how challenging is this task in unconstrained scenarios;and finally iv) we announce a novel public database, named iFakeFaceDB, yielding from the application of our proposed GAN-fingerprint Removal approach (GANprintR) to already very realistic synthetic fake images. The results obtained in our empirical evaluation show that additional efforts are required to develop robust facial manipulation detection systems against unseen conditions and spoof techniques, such as the one proposed in this study. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

Children Age Group Detection based on Human-Computer Interaction and Time Series Analysis

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arXiv 2024年

作者： Ruiz-Garcia, Juan Carlos Hojas, Carlos Tolosana, Ruben Vera-Rodriguez, Ruben Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Herreros-Rodriguez, Jaime The Biometrics and Data Pattern Analytics - BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain The Hospital Universitario Infanta Leonor Madrid28031 Spain

This article proposes a novel Children-Computer Interaction (CCI) approach for the task of age group detection. This approach focuses on the automatic analysis of the time series generated from the interaction of the children with mobile devices. In particular, we extract a set of 25 time series related to spatial, pressure, and kinematic information of the children interaction while colouring a tree through a pen stylus tablet, a specific test from the large-scale public ChildCIdb database1. A complete analysis of the proposed approach is carried out using different time series selection techniques to choose the most discriminative ones for the age group detection task: i) a statistical analysis, and ii) an automatic algorithm called Sequential Forward Search (SFS). In addition, different classification algorithms such as Dynamic Time Warping Barycenter Averaging (DBA) and Hidden Markov Models (HMM) are studied. Accuracy results over 85% are achieved, outperforming previous approaches in the literature and in more challenging age group conditions. Finally, the approach presented in this study can benefit many children-related applications, for example, towards an age-appropriate environment with the technology. © 2024, CC BY-NC-ND.

关键词： Time series analysis