Abstract

There is an increasing demand for applications that can detect changes in human affect or behavior especially in the fields of health care and crime detection. Detection of changes in continuous human affect dimensions from multimedia data precedes the exact prediction of an emotion as a continuum. With the growth in the dimensions of emotion space there is a need to discover latent descriptors (topics) that can explain these complex states. Considering that at every time step the audio/video frames constitute a set of such latent topics, the presence and absence of changes in emotion should effect the topics in those frames. Based on this assumption an Adaptive Temporal Topic model (ATTM) based change detection algorithm is presented that, at each time step, detects whether a significant change in human affect has occurred. ATTM is a probabilistic topic model that extends Latent Dirichlet Allocation model by incorporating the temporal dependencies between human audio/video 'documents' and generates refined topics. The topics assigned to a document by ATTM are adapted to the presence or absence of a change in the affect dimension at that time step. ATTM along with different regression models has been tested on the multimodal Audio Visual Emotion Challenge (AVEC 2012) data and has shown promising results in comparison to existing temporal and non-temporal topic models.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Multimedia and Expo, ICME 2013
DOIs
StatePublished - Oct 21 2013
Event2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, United States
Duration: Jul 15 2013Jul 19 2013

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Other

Other2013 IEEE International Conference on Multimedia and Expo, ICME 2013
CountryUnited States
CitySan Jose, CA
Period7/15/137/19/13

Keywords

  • Change Detection
  • Human Emotion Recognition algorithm
  • Topic Models
  • Video Audio data

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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  • Cite this

    Lade, P., Balasubramanian, V. N., Demakethepalli Venkateswara, H., & Panchanathan, S. (2013). Detection of changes in human affect dimensions using an Adaptive Temporal Topic model. In 2013 IEEE International Conference on Multimedia and Expo, ICME 2013 [6607627] (Proceedings - IEEE International Conference on Multimedia and Expo). https://doi.org/10.1109/ICME.2013.6607627