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Leveraging automatic personalised nutrition: food image recognition benchmark and dataset based on nutrition taxonomy

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Multimedia Tools and Applications 2025年第4期84卷 1945-1966页

作者： Romero-Tapiador, Sergio Tolosana, Ruben Morales, Aythami Fierrez, Julian Vera-Rodriguez, Ruben Espinosa-Salinas, Isabel Freixer, Gala Carrillo de Santa Pau, Enrique Ramírez de Molina, Ana Ortega-Garcia, Javier Biometrics and Data Pattern Analytics Laboratory Universidad Autonoma de Madrid Madrid28049 Spain IMDEA Food Institute CEI UAM+CSIC Madrid28049 Spain

Maintaining a healthy lifestyle has become increasingly challenging in today’s sedentary society marked by poor eating habits. To address this issue, both national and international organisations have made numerous efforts to promote healthier diets and increased physical activity. However, implementing these recommendations in daily life can be difficult, as they are often generic and not tailored to individuals. This study presents the AI4Food-NutritionDB database, the first nutrition database that incorporates food images and a nutrition taxonomy based on recommendations by national and international health authorities. The database offers a multi-level categorisation, comprising 6 nutritional levels, 19 main categories (e.g., "Meat"), 73 subcategories (e.g., "White Meat"), and 893 specific food products (e.g., "Chicken"). The AI4Food-NutritionDB opens the doors to new food computing approaches in terms of food intake frequency, quality, and categorisation. Also, we present a standardised experimental protocol and benchmark including three tasks based on the nutrition taxonomy (i.e., category, subcategory, and final product recognition). These resources are available to the research community, including our deep learning models trained on AI4Food-NutritionDB, which can serve as pre-trained models, achieving accurate recognition results for challenging food image databases. All these resources are available in GitHub (https://***/BiDAlab/AI4Food-NutritionDB). © The Author(s) 2024.

关键词： Nutrition

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OTB-morph:One-time biometrics via Morphing

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Machine Intelligence Research 2023年第6期20卷 855-871页

作者： Mahdi Ghafourian Julian Fierrez Ruben Vera-Rodriguez Aythami Morales Ignacio Serna Biometrics and Data Pattern Analytics Laboratory(BiDA Lab) Universidad Autonoma de MadridMadrid28049Spain

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 *** transformation is repeatable enabling subsequent biometric *** paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization *** proposed scheme is based on time-varying keys(random biometrics in our case)and morphing *** experimental implementation of the proposed scheme is given for face *** results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.

关键词： biometrics face recognition template protection morphing security

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DeepFace-Attention: Multimodal Face biometrics for Attention Estimation with Application to e-Learning

arXiv

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arXiv 2024年

作者： Daza, Roberto Gomez, Luis F. Fierrez, Julian Morales, Aythami Tolosana, Ruben Ortega-Garcia, Javier Biometrics and Data Pattern Analytics Laboratory Universidad Autonoma de Madrid Campus de Cantoblanco Madrid28049 Spain

This work introduces an innovative method for estimating attention levels (cognitive load) using an ensemble of facial analysis techniques applied to webcam videos. Our method is particularly useful, among others, in e-learning applications, so we trained, evaluated, and compared our approach on the mEBAL2 database, a public multi-modal database acquired in an e-learning environment. mEBAL2 comprises data from 60 users who performed 8 different tasks. These tasks varied in difficulty, leading to changes in their cognitive loads. Our approach adapts state-of-the-art facial analysis technologies to quantify the users’ cognitive load in the form of high or low attention. Several behavioral signals and physiological processes related to the cognitive load are used, such as eyeblink, heart rate, facial action units, and head pose, among others. Furthermore, we conduct a study to understand which individual features obtain better results, the most efficient combinations, explore local and global features, and how temporary time intervals affect attention level estimation, among other aspects. We find that global facial features are more appropriate for multimodal systems using score-level fusion, particularly as the temporal window increases. On the other hand, local features are more suitable for fusion through neural network training with score-level fusion approaches. Our method outperforms existing state-of-the-art accuracies using the public mEBAL2 benchmark. Copyright © 2024, The Authors. All rights reserved.

关键词： Heart

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Synthetic data for the Mitigation of Demographic Biases in Face Recognition

Synthetic Data for the Mitigation of Demographic Biases in F...

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IEEE International Joint Conference on biometrics (IJCB)

作者： Pietro Melzi Christian Rathgeb Ruben Tolosana Ruben Vera-Rodriguez Aythami Morales Dominik Lawatsch Florian Domin Maxim Schaubert 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.

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GANDiffFace: Controllable Generation of Synthetic datasets for Face Recognition with Realistic Variations

GANDiffFace: Controllable Generation of Synthetic Datasets f...

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International Conference on Computer Vision Workshops (ICCV Workshops)

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

Face recognition systems have significantly advanced in recent years, driven by the availability of large-scale datasets. However, several issues have recently came up, including privacy concerns that have led to the discontinuation of well-established public datasets. Synthetic datasets have emerged as a solution, even though current synthesis methods present other drawbacks such as limited intraclass variations, lack of realism, and unfair representation of demographic groups. This study introduces GAN-DiffFace, a novel framework for the generation of synthetic datasets for face recognition that combines the power of Generative Adversarial Networks (GANs) and Diffusion models to overcome the limitations of existing synthetic datasets. In GANDiffFace, we first propose the use of GANs to synthesize highly realistic identities and meet target demographic distributions. Subsequently, we fine-tune Diffusion models with the images generated with GANs, synthesizing multiple images of the same identity with a variety of accessories, poses, expressions, and contexts. We generate multiple synthetic datasets by changing GANDiffFace settings, and compare their mated and non-mated score distributions with the distributions provided by popular real-world datasets for face recognition, i.e. VGG2 and IJB-C. Our results show the feasibility of the proposed GANDiffFace, in particular the use of Diffusion models to enhance the (limited) intra-class variations provided by GANs towards the level of real-world datasets.

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GANDiffFace: Controllable Generation of Synthetic datasets for Face Recognition with Realistic Variations

arXiv

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arXiv 2023年

作者： Melzi, Pietro Rathgeb, Christian Tolosana, Ruben Vera-Rodriguez, Ruben 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

Face recognition systems have significantly advanced in recent years, driven by the availability of large-scale datasets. However, several issues have recently came up, including privacy concerns that have led to the discontinuation of well-established public datasets. Synthetic datasets have emerged as a solution, even though current synthesis methods present other drawbacks such as limited intra-class variations, lack of realism, and unfair representation of demographic groups. This study introduces GANDiffFace, a novel framework for the generation of synthetic datasets for face recognition that combines the power of Generative Adversarial Networks (GANs) and Diffusion models to overcome the limitations of existing synthetic datasets. In GANDiffFace, we first propose the use of GANs to synthesize highly realistic identities and meet target demographic distributions. Subsequently, we fine-tune Diffusion models with the images generated with GANs, synthesizing multiple images of the same identity with a variety of accessories, poses, expressions, and contexts. We generate multiple synthetic datasets by changing GANDiffFace settings, and compare their mated and non-mated score distributions with the distributions provided by popular real-world datasets for face recognition, i.e. VGG2 and IJB-C. Our results show the feasibility of the proposed GANDiffFace, in particular the use of Diffusion models to enhance the (limited) intra-class variations provided by GANs towards the level of real-world datasets. © 2023, CC BY-NC-ND.

关键词： Face recognition

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Synthetic data for the Mitigation of Demographic Biases in Face Recognition

arXiv

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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

关键词： Face recognition

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mEBAL2 database and Benchmark: Image-based Multispectral Eyeblink Detection

arXiv

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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

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ICDAR 2021 competition on on-line signature verification

arXiv

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arXiv 2021年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Gonzalez-Garcia, Carlos Fierrez, Julian Rengifo, Santiago Morales, Aythami Ortega-Garcia, Javier Ruiz-Garcia, Juan Carlos Romero-Tapiador, Sergio Jiang, Jiajia Lai, Songxuan Jin, Lianwen Zhu, Yecheng Galbally, Javier Diaz, Moises Ferrer, Miguel Angel Gomez-Barrero, Marta Hodashinsky, Ilya Sarin, Konstantin Slezkin, Artem Bardamova, Marina Svetlakov, Mikhail Saleem, Mohammad Szücs, Cintia Lia Kovari, Bence Pulsmeyer, Falk Wehbi, Mohamad Zanca, Dario Ahmad, Sumaiya Mishra, Sarthak Jabin, Suraiya Biometrics and Data Pattern Analytics Lab UAM Spain South China University of Technology China Guangdong Artificial Intelligence and Digital Economy Laboratory China European Commission Joint Research Centre Italy Universidad del Atlantico Medio Spain Universidad de las Palmas de Gran Canaria Spain Hochschule Ansbach Germany Tomsk State University of Control Systems and Radioelectronics Russia Budapest University of Technology and Economics Hungary Machine Learning and Data Analytics Lab FAU Germany Jamia Millia Islamia India

This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). SVC 2021 will be established as an on-going competition12, where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases such as DeepSignDB13 and SVC2021_EvalDB14, and standard experimental protocols. © 2021, CC BY-NC-ND.

关键词： Deep learning

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