A CNN Cascade for Landmark Guided Semantic Part Segmentation

  • Aaron S. Jackson
  • Michel Valstar
  • Georgios Tzimiropoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9915)

Abstract

This paper proposes a CNN cascade for semantic part segmentation guided by pose-specific information encoded in terms of a set of landmarks (or keypoints). There is large amount of prior work on each of these tasks separately, yet, to the best of our knowledge, this is the first time in literature that the interplay between pose estimation and semantic part segmentation is investigated. To address this limitation of prior work, in this paper, we propose a CNN cascade of tasks that firstly performs landmark localisation and then uses this information as input for guiding semantic part segmentation. We applied our architecture to the problem of facial part segmentation and report large performance improvement over the standard unguided network on the most challenging face datasets. Testing code and models will be published online at http://cs.nott.ac.uk/~psxasj/.

Keywords

Pose estimation Landmark localisation Semantic part segmentation Faces 

Notes

Acknowledgements

Aaron Jackson was funded by a PhD scholarship from the University of Nottingham. The work of Valstar is also funded by European Union Horizon 2020 research and innovation programme under grant agreement number 645378. Georgios Tzimiropoulos was supported in part by the EPSRC project EP/M02153X/1 Facial Deformable Models of Animals.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Aaron S. Jackson
    • 1
  • Michel Valstar
    • 1
  • Georgios Tzimiropoulos
    • 1
  1. 1.School of Computer ScienceThe University of NottinghamNottinghamUK

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