A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage
In the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been propo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/7/2101 |
| _version_ | 1797571185077125120 |
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| author | Roberto Pierdicca Marina Paolanti Ramona Quattrini Marco Mameli Emanuele Frontoni |
| author_facet | Roberto Pierdicca Marina Paolanti Ramona Quattrini Marco Mameli Emanuele Frontoni |
| author_sort | Roberto Pierdicca |
| collection | DOAJ |
| description | In the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been proposed that is learned from eye tracking data. In particular, eye-tracking data of adults and children observing five paintings with similar characteristics have been collected. The images are selected by CH experts and are—the three “Ideal Cities” (Urbino, Baltimore and Berlin), the Inlaid chest in the National Gallery of Marche and Wooden panel in the “Studiolo del Duca” with Marche view. These pictures have been recognized by experts as having analogous features thus providing coherent visual stimuli. Our proposed method combines a new coordinates representation from eye sequences by using Geometric Algebra with a deep learning model for automated recognition (to identify, differentiate, or authenticate individuals) of people by the attention focus of distinctive eye movement patterns. The experiments were conducted by comparing five Deep Convolutional Neural Networks (DCNNs), yield high accuracy (more than <inline-formula> <math display="inline"> <semantics> <mrow> <mn>80</mn> <mo>%</mo></mrow></semantics></math></inline-formula>), demonstrating the effectiveness and suitability of the proposed approach in identifying adults and children as museums’ visitors. |
| first_indexed | 2024-03-10T20:35:46Z |
| format | Article |
| id | doaj.art-5a898258df764fab84c6b222e5484a51 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:35:46Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5a898258df764fab84c6b222e5484a512023-11-19T21:02:06ZengMDPI AGSensors1424-82202020-04-01207210110.3390/s20072101A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural HeritageRoberto Pierdicca0Marina Paolanti1Ramona Quattrini2Marco Mameli3Emanuele Frontoni4Dipartimento di Ingegneria Civile, Edile e dell’Architettura, Universitá Politecnica delle Marche, 60131 Ancona, ItalyDipartimento di Ingegneria dell’Informazione, Universitá Politecnica delle Marche, 60131 Ancona, ItalyDipartimento di Ingegneria Civile, Edile e dell’Architettura, Universitá Politecnica delle Marche, 60131 Ancona, ItalyDipartimento di Ingegneria dell’Informazione, Universitá Politecnica delle Marche, 60131 Ancona, ItalyDipartimento di Ingegneria dell’Informazione, Universitá Politecnica delle Marche, 60131 Ancona, ItalyIn the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been proposed that is learned from eye tracking data. In particular, eye-tracking data of adults and children observing five paintings with similar characteristics have been collected. The images are selected by CH experts and are—the three “Ideal Cities” (Urbino, Baltimore and Berlin), the Inlaid chest in the National Gallery of Marche and Wooden panel in the “Studiolo del Duca” with Marche view. These pictures have been recognized by experts as having analogous features thus providing coherent visual stimuli. Our proposed method combines a new coordinates representation from eye sequences by using Geometric Algebra with a deep learning model for automated recognition (to identify, differentiate, or authenticate individuals) of people by the attention focus of distinctive eye movement patterns. The experiments were conducted by comparing five Deep Convolutional Neural Networks (DCNNs), yield high accuracy (more than <inline-formula> <math display="inline"> <semantics> <mrow> <mn>80</mn> <mo>%</mo></mrow></semantics></math></inline-formula>), demonstrating the effectiveness and suitability of the proposed approach in identifying adults and children as museums’ visitors.https://www.mdpi.com/1424-8220/20/7/2101eye-trackingDigital Cultural HeritageDeep Convolutional Neural Networks |
| spellingShingle | Roberto Pierdicca Marina Paolanti Ramona Quattrini Marco Mameli Emanuele Frontoni A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage Sensors eye-tracking Digital Cultural Heritage Deep Convolutional Neural Networks |
| title | A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage |
| title_full | A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage |
| title_fullStr | A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage |
| title_full_unstemmed | A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage |
| title_short | A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage |
| title_sort | visual attentive model for discovering patterns in eye tracking data a proposal in cultural heritage |
| topic | eye-tracking Digital Cultural Heritage Deep Convolutional Neural Networks |
| url | https://www.mdpi.com/1424-8220/20/7/2101 |
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