Attention Span Prediction Using Head-Pose Estimation with Deep Neural Networks

Singh, T; Mohadikar, M; Gite, S; Patil, S; Pradhan, B; Alamri, A

Attention Span Prediction Using Head-Pose Estimation with Deep Neural Networks

Singh, T Mohadikar, M Gite, S Patil, S Pradhan, B

Alamri, A

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Access, 2021, 9, pp. 142632-142643
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Singh, T
dc.contributor.author	Mohadikar, M
dc.contributor.author	Gite, S
dc.contributor.author	Patil, S
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Alamri, A
dc.date.accessioned	2022-02-17T23:57:18Z
dc.date.available	2022-02-17T23:57:18Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Access, 2021, 9, pp. 142632-142643
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/154665
dc.description.abstract	Automated human pose estimation is evolving as an exciting research area in human activity detection. It includes sophisticated applications such as malpractice detection in the examination, distracted driving, gesture detection, etc., and requires robust and reliable pose estimation techniques. These applications help to map the attention of the user with head pose estimation (HPE) metrics supported by emotion and gaze analysis. This paper solves the problem of attention score estimation with HPE. The proposed method ensures ease of implementation while addressing head pose estimation using 68 facial features. Further, to attain reliability and precision, head pose estimation has been implemented as a regression task. The coordinate pair angle method (CPAM) with deep neural network (DNN) regression and elastic net regression is carried out. The use of DNN ensures precision on low lighting, distorted or occluded images. CPAM methodology leverages facial landmark detection and angular difference to estimate head pose. Experimentation results showed that the proposed model could handle large datasets, real-time data processing, significant pose variations, partial occlusions, and diverse facial expressions with a mean absolute error (MAE) of 3° and less. The proposed system was evaluated on three standard databases: the 300W across large poses (300W-LP) dataset, annotated facial landmarks in the wild (AFLW2000) dataset, and the national institute of mental health child emotional faces picture set (NIMH-ChEFS) dataset. The results achieved are on par with recent state-of-the-art methodologies such as anisotropic angle distribution learning (AADL), joint head pose estimation and face alignment algorithm (JFA), rotation axis focused attention network (RAFA-Net), and propose an MAE ranging up to 6°. The paper could achieve remarkable results for attention span prediction using head pose estimation and for many possible future applications.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2021.3120098
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	Attention Span Prediction Using Head-Pose Estimation with Deep Neural Networks
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-17T23:57:17Z
pubs.publication-status	Published
pubs.volume	9

Abstract:

Automated human pose estimation is evolving as an exciting research area in human activity detection. It includes sophisticated applications such as malpractice detection in the examination, distracted driving, gesture detection, etc., and requires robust and reliable pose estimation techniques. These applications help to map the attention of the user with head pose estimation (HPE) metrics supported by emotion and gaze analysis. This paper solves the problem of attention score estimation with HPE. The proposed method ensures ease of implementation while addressing head pose estimation using 68 facial features. Further, to attain reliability and precision, head pose estimation has been implemented as a regression task. The coordinate pair angle method (CPAM) with deep neural network (DNN) regression and elastic net regression is carried out. The use of DNN ensures precision on low lighting, distorted or occluded images. CPAM methodology leverages facial landmark detection and angular difference to estimate head pose. Experimentation results showed that the proposed model could handle large datasets, real-time data processing, significant pose variations, partial occlusions, and diverse facial expressions with a mean absolute error (MAE) of 3° and less. The proposed system was evaluated on three standard databases: the 300W across large poses (300W-LP) dataset, annotated facial landmarks in the wild (AFLW2000) dataset, and the national institute of mental health child emotional faces picture set (NIMH-ChEFS) dataset. The results achieved are on par with recent state-of-the-art methodologies such as anisotropic angle distribution learning (AADL), joint head pose estimation and face alignment algorithm (JFA), rotation axis focused attention network (RAFA-Net), and propose an MAE ranging up to 6°. The paper could achieve remarkable results for attention span prediction using head pose estimation and for many possible future applications.

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