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Facial Landmarks Detection Using a Cascade of Recombinator Networks

  • Pedro Diego López
  • Roberto ValleEmail author
  • Luis Baumela
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

Nowadays, Convolutional Neural Nets (CNNs) have become the reference technology for many computer vision problems, including facial landmarks detection. Although CNNs are very robust, they still lack accuracy because they cannot enforce the estimated landmarks to represent a valid face shape.

In this paper we investigate the use of a cascade of CNN regressors to make the set of estimated landmarks lie closer to a valid face shape. To this end, we introduce CRN, a facial landmarks detection algorithm based on a Cascade of Recombinator Networks. The proposed approach not only improves the baseline model, but also achieves state-of-the-art results in 300W, COFW and AFLW that are widely considered the most challenging public data sets.

Keywords

Face alignment Cascaded shape regression Convolutional neural networks 

Notes

Acknowledgments

Authors acknowledge funding from the Spanish Ministry of Economy and Competitiveness under project TIN2016-75982-C2-2-R.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pedro Diego López
    • 1
  • Roberto Valle
    • 1
    Email author
  • Luis Baumela
    • 1
  1. 1.Univ. Politécnica MadridMadridSpain

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