Estimate gaze density by incorporating emotion

Huiying Liu, Min Xu, Xiangjian He, Jinqiao Wang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Gaze density estimation has attracted many research efforts in the past years. The factors considered in the existing methods include low level feature saliency, spatial position, and objects. Emotion, as an important factor driving attention, has not been taken into account. In this paper, we are the first to estimate gaze density through incorporating emotion. To estimate the emotion intensity of each position in an image, we consider three aspects, generic emotional content, facial expression intensity, and emotional objects. Generic emotional content is estimated by using Multiple instance learning, which is employed to train an emotion detector from weakly labeled images. Facial expression intensity is estimated by using a ranking method. Emotional objects are detected, by taking blood/injury and worm/snake as examples. Finally, emotion intensity, low level feature saliency, and spatial position, are fused, through a linear support vector machine, to estimate gaze density. The performance is tested on public eye tracking dataset. Experimental results indicate that incorporating emotion does improve the performance of gaze density estimation.

Original languageEnglish
Title of host publicationMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450330633
Publication statusPublished - 3 Nov 2014
Externally publishedYes
Event2014 ACM Conference on Multimedia, MM 2014 - Orlando, United States
Duration: 3 Nov 20147 Nov 2014

Publication series

NameMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia


Conference2014 ACM Conference on Multimedia, MM 2014
Country/TerritoryUnited States


  • Emotion
  • Visual attention
  • Visual saliency

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Software


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