Dynamic Probabilistic CCA for Analysis of Affective Behaviour

  • Mihalis A. Nicolaou
  • Vladimir Pavlovic
  • Maja Pantic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7578)

Abstract

Fusing multiple continuous expert annotations is a crucial problem in machine learning and computer vision, particularly when dealing with uncertain and subjective tasks related to affective behaviour. Inspired by the concept of inferring shared and individual latent spaces in probabilistic CCA (PCCA), we firstly propose a novel, generative model which discovers temporal dependencies on the shared/individual spaces (DPCCA). In order to accommodate for temporal lags which are prominent amongst continuous annotations, we further introduce a latent warping process. We show that the resulting model (DPCTW) (i) can be used as a unifying framework for solving the problems of temporal alignment and fusion of multiple annotations in time, and (ii) that by incorporating dynamics, modelling annotation/sequence specific biases, noise estimation and time warping, DPCTW outperforms state-of-the-art methods for both the aggregation of multiple, yet imperfect expert annotations as well as the alignment of affective behavior.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mihalis A. Nicolaou
    • 1
  • Vladimir Pavlovic
    • 2
  • Maja Pantic
    • 1
    • 3
  1. 1.Dept. of ComputingImperial College LondonUK
  2. 2.Dept. of Computer ScienceRutgers UniversityUSA
  3. 3.EEMCSUniversity of TwenteThe Netherlands

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