TY  - JOUR
AU  - Pepe, Sveva
AU  - Tedeschi, Simone
AU  - Brandizzi, Nicolo'
AU  - Russo, Samuele
AU  - Iocchi, Luca
AU  - Napoli, Christian
PY  - 2022
DA  - 2022/10/04
TI  - Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset
JO  - OBM Neurobiology
SP  - 139
VL  - 06
IS  - 04
AB  - 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.
SN  - 2573-4407
UR  - https://doi.org/10.21926/obm.neurobiol.2204139
DO  - 10.21926/obm.neurobiol.2204139
ID  - Pepe2022
ER  -