Cumulative attributes for pain intensity estimation
Pain estimation from face video is a hard problem in automatic behaviour understanding. One major obstacle is the di culty of collecting sufficient amounts of data, with balanced amounts of data for all pain intensity levels. To overcome this, we propose to adopt Cumulative Attributes, which assume...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2017
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| Online Access: | https://eprints.nottingham.ac.uk/45830/ |
| _version_ | 1848797201556307968 |
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| author | Joy, Egede Michel, Valstar |
| author_facet | Joy, Egede Michel, Valstar |
| author_sort | Joy, Egede |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Pain estimation from face video is a hard problem in automatic behaviour understanding. One major obstacle is the di culty of collecting sufficient amounts of data, with balanced amounts of data for all pain intensity levels. To overcome this, we propose to adopt Cumulative Attributes, which assume that attributes for high pain levels with few examples are a superset of all attributes of lower pain levels. Experimental results show a consistent relative performance increase in the order of 20% regardless of features used. Our final system significantly outperforms the state of the art on the UNBC McMaster Shoulder Pain database by using cumulative attributes with Relevance Vector Regression on a combination of features, including appearance, geometric, and deep learned features. |
| first_indexed | 2025-11-14T20:00:07Z |
| format | Conference or Workshop Item |
| id | nottingham-45830 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:00:07Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-458302020-05-04T19:17:29Z https://eprints.nottingham.ac.uk/45830/ Cumulative attributes for pain intensity estimation Joy, Egede Michel, Valstar Pain estimation from face video is a hard problem in automatic behaviour understanding. One major obstacle is the di culty of collecting sufficient amounts of data, with balanced amounts of data for all pain intensity levels. To overcome this, we propose to adopt Cumulative Attributes, which assume that attributes for high pain levels with few examples are a superset of all attributes of lower pain levels. Experimental results show a consistent relative performance increase in the order of 20% regardless of features used. Our final system significantly outperforms the state of the art on the UNBC McMaster Shoulder Pain database by using cumulative attributes with Relevance Vector Regression on a combination of features, including appearance, geometric, and deep learned features. 2017-11-13 Conference or Workshop Item PeerReviewed Joy, Egede and Michel, Valstar (2017) Cumulative attributes for pain intensity estimation. In: 19th ACM International Conference on Multimodal Interaction, 13-17 November 2017, Glasgow, Scotland. (In Press) Pain estimation; Attribute learning; Multi-output regression; Relevance Vector Machines (RVM) |
| spellingShingle | Pain estimation; Attribute learning; Multi-output regression; Relevance Vector Machines (RVM) Joy, Egede Michel, Valstar Cumulative attributes for pain intensity estimation |
| title | Cumulative attributes for pain intensity estimation |
| title_full | Cumulative attributes for pain intensity estimation |
| title_fullStr | Cumulative attributes for pain intensity estimation |
| title_full_unstemmed | Cumulative attributes for pain intensity estimation |
| title_short | Cumulative attributes for pain intensity estimation |
| title_sort | cumulative attributes for pain intensity estimation |
| topic | Pain estimation; Attribute learning; Multi-output regression; Relevance Vector Machines (RVM) |
| url | https://eprints.nottingham.ac.uk/45830/ |