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Detection of changes in human affect dimensions using an Adaptive Temporal Topic model | IEEE Conference Publication | IEEE Xplore

Detection of changes in human affect dimensions using an Adaptive Temporal Topic model


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 ...Show More

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.
Date of Conference: 15-19 July 2013
Date Added to IEEE Xplore: 26 September 2013
Electronic ISBN:978-1-4799-0015-2

ISSN Information:

Conference Location: San Jose, CA, USA

References

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