Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset
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Published:2022-10-04
Issue:4
Volume:6
Page:1-1
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ISSN:2573-4407
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Container-title:OBM Neurobiology
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language:
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Short-container-title:OBM Neurobiol
Author:
Napoli Christian,Iocchi Luca,Russo Samuele,Brandizzi Nicolo,Tedeschi Simone,Pepe Sveva
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.
Publisher
LIDSEN Publishing Inc
Subject
Cellular and Molecular Neuroscience,Neurology (clinical),Developmental Neuroscience,Neurology
Cited by
1 articles.
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