CTM

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This service operates in key areas within modern communications networks and services, especially in network architectures, telecommunications services, signal and image processing, microelectronics, digital TV and multimedia.

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Browsing CTM by Author "3889"
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    802.11 wireless simulation and anomaly detection using HMM and UBM
    ( 2020) Anisa Allahdadidastjerdi ; Ricardo Morla ; Jaime Cardoso ; 5587 ; 3645 ; 3889
    Despite the growing popularity of 802.11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior, among other reasons. Anomaly detection and distinction are in the thick of major challenges that network managers encounter. The difficulty of monitoring broad and complex Wireless Local Area Networks, that often requires heavy instrumentation of the user devices, makes anomaly detection analysis even harder. In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model (HMM) and Universal Background Model (UBM) on data that is inexpensive to obtain. We then generate a number of network anomalous scenarios in OMNeT /INET network simulator and compare the detection outcomes with those in baseline approaches—RawData and Principal Component Analysis. The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.
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    Audiovisual Classification of Group Emotion Valence Using Activity Recognition Networks
    ( 2020) Pinto,C ; Jaime Cardoso ; Carvalho,P ; Gonçalves,F ; Fonseca,J ; João Tiago Pinto ; Tiago Filipe Gonçalves ; Sanhudo,L ; 3889 ; 7804 ; 7250
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    Deep Anomaly Detection for In-Vehicle Monitoring—An Application-Oriented Review
    ( 2022) Caetano,F ; Pedro Miguel Carvalho ; Jaime Cardoso ; 3889 ; 4358
    Anomaly detection has been an active research area for decades, with high application potential. Recent work has explored deep learning approaches to the detection of abnormal behaviour and abandoned objects in outdoor video surveillance scenarios. The extension of this recent work to in-vehicle monitoring using solely visual data represents a relevant research opportunity that has been overlooked in the accessible literature. With the increasing importance of public and shared transportation for urban mobility, it becomes imperative to provide autonomous intelligent systems capable of detecting abnormal behaviour that threatens passenger safety. To investigate the applicability of current works to this scenario, a recapitulation of relevant state-of-the-art techniques and resources is presented, including available datasets for their training and benchmarking. The lack of public datasets dedicated to in-vehicle monitoring is addressed alongside other issues not considered in previous works, such as moving backgrounds and frequent illumination changes. Despite its relevance, similar surveys and reviews have disregarded this scenario and its specificities. This work initiates an important discussion on application-oriented issues, proposing solutions to be followed in future works, particularly synthetic data augmentation to achieve representative instances with the low amount of available sequences.
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    Deep Neural Networks for Biometric Identification Based on Non-Intrusive ECG Acquisitions
    ( 2019) Jaime Cardoso ; João Tiago Pinto ; Lourenço,A ; 7250 ; 3889
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    Don’t You Forget About Me: A Study on Long-Term Performance in ECG Biometrics
    ( 2019) João Tiago Pinto ; Lopes,G ; Jaime Cardoso ; 3889 ; 7250
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    ECG Biometrics
    ( 2021) Jaime Cardoso ; João Tiago Pinto ; 7250 ; 3889
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    An End-to-End Convolutional Neural Network for ECG-Based Biometric Authentication
    ( 2019) Jaime Cardoso ; João Tiago Pinto ; 7250 ; 3889
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    Evolution, Current Challenges, and Future Possibilities in ECG Biometrics
    ( 2018) João Tiago Pinto ; Jaime Cardoso ; Lourenco,A ; 7250 ; 3889
    Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.
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    Explaining ECG Biometrics: Is It All In The QRS?
    ( 2020) João Tiago Pinto ; Jaime Cardoso ; 3889 ; 7250
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    Interpretable Biometrics: Should We Rethink How Presentation Attack Detection is Evaluated?
    ( 2020) Ana Filipa Sequeira ; Jaime Cardoso ; Tiago Filipe Gonçalves ; João Tiago Pinto ; Wilson Santos Silva ; 6661 ; 3889 ; 5461 ; 7250 ; 7804
    Presentation attack detection (PAD) methods are commonly evaluated using metrics based on the predicted labels. This is a limitation, especially for more elusive methods based on deep learning which can freely learn the most suitable features. Though often being more accurate, these models operate as complex black boxes which makes the inner processes that sustain their predictions still baffling. Interpretability tools are now being used to delve deeper into the operation of machine learning methods, especially artificial networks, to better understand how they reach their decisions. In this paper, we make a case for the integration of interpretability tools in the evaluation of PAD. A simple model for face PAD, based on convolutional neural networks, was implemented and evaluated using both traditional metrics (APCER, BPCER and EER) and interpretability tools (Grad-CAM), using data from the ROSE Youtu video collection. The results show that interpretability tools can capture more completely the intricate behavior of the implemented model, and enable the identification of certain properties that should be verified by a PAD method that is robust, coherent, meaningful, and can adequately generalize to unseen data and attacks. One can conclude that, with further efforts devoted towards higher objectivity in interpretability, this can be the key to obtain deeper and more thorough PAD performance evaluation setups. © 2020 IEEE.
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    MFR 2021: Masked Face Recognition Competition
    ( 2021) Han,D ; Aginako,N ; Sierra,B ; Nieto,M ; Erakin,ME ; Demir,U ; Ekenel,HK ; Kataoka,A ; Ichikawa,K ; Kubo,S ; Zhang,J ; He,M ; Montero,D ; Shan,S ; Grm,K ; Struc,V ; Seneviratne,S ; Kasthuriarachchi,N ; Rasnayaka,S ; Pedro David Carneiro ; Sequeira,AF ; João Tiago Pinto ; Saffari,M ; Jaime Cardoso ; Boutros,F ; Damer,N ; Kolf,JN ; Raja,K ; Kirchbuchner,F ; Ramachandra,R ; Kuijper,A ; Fang,P ; Zhang,C ; Wang,F ; 3889 ; 7250 ; 8292
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    Mixture-Based Open World Face Recognition
    ( 2021) João Tiago Pinto ; Matta,A ; Jaime Cardoso ; 3889 ; 7250
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    Secure Triplet Loss for End-to-End Deep Biometrics
    ( 2020) Miguel Velhote Correia ; João Tiago Pinto ; Jaime Cardoso ; 4996 ; 3889 ; 7250
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    Secure Triplet Loss: Achieving Cancelability and Non-Linkability in End-to-End Deep Biometrics
    ( 2021) João Tiago Pinto ; Miguel Velhote Correia ; Jaime Cardoso ; 3889 ; 4996 ; 7250
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    Self-Learning with Stochastic Triplet Loss
    ( 2020) Jaime Cardoso ; João Tiago Pinto ; 7250 ; 3889
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    Streamlining Action Recognition in Autonomous Shared Vehicles with an Audiovisual Cascade Strategy
    ( 2022) João Tiago Pinto ; Pedro Miguel Carvalho ; Pinto,C ; Sousa,A ; Leonardo Gomes Capozzi ; Jaime Cardoso ; 3889 ; 4358 ; 7250 ; 8288
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    Towards vehicle occupant-invariant models for activity characterisation
    ( 2022) Leonardo Gomes Capozzi ; Barbosa,V ; Pinto,C ; João Tiago Pinto ; Américo José Pereira ; Pedro Miguel Carvalho ; Jaime Cardoso ; 3889 ; 4358 ; 6078 ; 7250 ; 8288