Probabilistic Subpixel Temporal Registration for Facial Expression Analysis

  • Evangelos Sariyanidi
  • Hatice Gunes
  • Andrea Cavallaro
Conference paper

DOI: 10.1007/978-3-319-16817-3_21

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9006)
Cite this paper as:
Sariyanidi E., Gunes H., Cavallaro A. (2015) Probabilistic Subpixel Temporal Registration for Facial Expression Analysis. In: Cremers D., Reid I., Saito H., Yang MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science, vol 9006. Springer, Cham

Abstract

Face images in a video sequence should be registered accurately before any analysis, otherwise registration errors may be interpreted as facial activity. Subpixel accuracy is crucial for the analysis of subtle actions. In this paper we present PSTR (Probabilistic Subpixel Temporal Registration), a framework that achieves high registration accuracy. Inspired by the human vision system, we develop a motion representation that measures registration errors among subsequent frames, a probabilistic model that learns the registration errors from the proposed motion representation, and an iterative registration scheme that identifies registration failures thus making PSTR aware of its errors. We evaluate PSTR’s temporal registration accuracy on facial action and expression datasets, and demonstrate its ability to generalise to naturalistic data even when trained with controlled data.

Supplementary material

Supplementary material (avi 17,790 KB)

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Evangelos Sariyanidi
    • 1
  • Hatice Gunes
    • 1
  • Andrea Cavallaro
    • 1
  1. 1.Centre for Intelligent SensingQueen Mary University of LondonLondonUK

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