DSpace Angular :: Browsing CTM - Indexed Articles in Conferences by Author "3889"

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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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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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An End-to-End Convolutional Neural Network for ECG-Based Biometric Authentication

( 2019) Jaime Cardoso ; João Tiago Pinto ; 7250 ; 3889

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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