Riesz-based volume local binary pattern and a novel group expression model for group happiness intensity analysis

Xiaohua Huang, Abhinav Dhall, Guoying Zhao, Roland Goecke, Matti Pietikainen

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a group of people. For group emotional intensity analysis, feature extraction and group expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new group expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new group expression model with the new feature. Our experimental results demonstrate the promising performance for group happiness intensity analysis.
Original languageEnglish
Title of host publicationProceedings of the British Machine Vision Conference 2015
EditorsXianghua Xie, Mark W. Jones, Gary K. L. Tam
Place of PublicationDurham UK
PublisherBritish Machine Vision Association
Number of pages13
ISBN (Electronic)1901725537
Publication statusPublished - 2015
EventBritish Machine Vision Conference 2015 - Swansea, United Kingdom
Duration: 7 Sept 201510 Sept 2015
Conference number: 26th


ConferenceBritish Machine Vision Conference 2015
Abbreviated titleBMVC 2015
Country/TerritoryUnited Kingdom
Internet address

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