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A Recurrent Encoder-Decoder Network for Sequential Face Alignment

  • Xi PengEmail author
  • Rogerio S. Feris
  • Xiaoyu Wang
  • Dimitris N. Metaxas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9905)

Abstract

We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment. Our proposed model predicts 2D facial point maps regularized by a regression loss, while uniquely exploiting recurrent learning at both spatial and temporal dimensions. At the spatial level, we add a feedback loop connection between the combined output response map and the input, in order to enable iterative coarse-to-fine face alignment using a single network model. At the temporal level, we first decouple the features in the bottleneck of the network into temporal-variant factors, such as pose and expression, and temporal-invariant factors, such as identity information. Temporal recurrent learning is then applied to the decoupled temporal-variant features, yielding better generalization and significantly more accurate results at test time. We perform a comprehensive experimental analysis, showing the importance of each component of our proposed model, as well as superior results over the state-of-the-art in standard datasets.

Keywords

Recurrent learning Encoder-decoder Face alignment 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Xi Peng
    • 1
    Email author
  • Rogerio S. Feris
    • 2
  • Xiaoyu Wang
    • 3
  • Dimitris N. Metaxas
    • 1
  1. 1.Rutgers UniversityPiscatawayUSA
  2. 2.IBM T. J. Watson Research CenterYorktown HeightsUSA
  3. 3.Snapchat ResearchVeniceUSA

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