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Failure Detection for Facial Landmark Detectors

  • Andreas Steger
  • Radu Timofte
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10117)

Abstract

Most face applications depend heavily on the accuracy of the face and facial landmarks detectors employed. Prediction of attributes such as gender, age, and identity usually completely fail when the faces are badly aligned due to inaccurate facial landmark detection. Despite the impressive recent advances in face and facial landmark detection, little study is on the recovery from and the detection of failures or inaccurate predictions. In this work we study two top recent facial landmark detectors and devise confidence models for their outputs. We validate our failure detection approaches on standard benchmarks (AFLW, HELEN) and correctly identify more than 40% of the failures in the outputs of the landmark detectors. Moreover, with our failure detection we can achieve a 12% error reduction on a gender estimation application at the cost of a small increase in computation.

Notes

Acknowledgment

This work was supported by the EU Framework 7 project ReMeDi (# 610902) and by the ETH General Fund (OK).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computer Vision Laboratory, D-ITETETH ZurichZürichSwitzerland

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