Human–robot interactions require the ability of the system to determine if the user is paying attention. However, to train such systems, massive amounts of data are required. In this study, we addressed the issue of data scarcity by constructing a large dataset (containing ~120,000 photographs) for the attention detection task. Then, by using this dataset, we established a powerful baseline system. In addition, we extended the proposed system by adding an auxiliary face detection module and introducing a unique GAN-based data augmentation technique. Experimental results revealed that the proposed system yields superior performance compared to baseline models and achieves an accuracy of 88% on the test set. Finally, we created a web application for testing the proposed model in real time.

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset / Pepe, S.; Tedeschi, S.; Brandizzi, N.; Russo, S.; Iocchi, L.; Napoli, C.. - In: OBM NEUROBIOLOGY. - ISSN 2573-4407. - 6:4(2022). [10.21926/obm.neurobiol.2204139]

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Brandizzi N.
Co-primo
Investigation
;
Russo S.
Co-primo
Conceptualization
;
Iocchi L.
Penultimo
Methodology
;
Napoli C.
Ultimo
Supervision
2022

Abstract

Human–robot interactions require the ability of the system to determine if the user is paying attention. However, to train such systems, massive amounts of data are required. In this study, we addressed the issue of data scarcity by constructing a large dataset (containing ~120,000 photographs) for the attention detection task. Then, by using this dataset, we established a powerful baseline system. In addition, we extended the proposed system by adding an auxiliary face detection module and introducing a unique GAN-based data augmentation technique. Experimental results revealed that the proposed system yields superior performance compared to baseline models and achieves an accuracy of 88% on the test set. Finally, we created a web application for testing the proposed model in real time.
2022
Attention assessment; dataset; GAN
01 Pubblicazione su rivista::01a Articolo in rivista
Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset / Pepe, S.; Tedeschi, S.; Brandizzi, N.; Russo, S.; Iocchi, L.; Napoli, C.. - In: OBM NEUROBIOLOGY. - ISSN 2573-4407. - 6:4(2022). [10.21926/obm.neurobiol.2204139]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1664524
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