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From Face Images and Attributes to Attributes

  • Robert Torfason
  • Eirikur Agustsson
  • Rasmus Rothe
  • Radu TimofteEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)

Abstract

The face is an important part of the identity of a person. Numerous applications benefit from the recent advances in prediction of face attributes, including biometrics (like age, gender, ethnicity) and accessories (eyeglasses, hat). We study the attributes’ relations to other attributes and to face images and propose prediction models for them. We show that handcrafted features can be as good as deep features, that the attributes themselves are powerful enough to predict other attributes and that clustering the samples according to their attributes can mitigate the training complexity for deep learning. We set new state-of-the-art results on two of the largest datasets to date, CelebA and Facebook BIG5, by predicting attributes either from face images, from other attributes, or from both face and other attributes. Particularly, on Facebook dataset, we show that we can accurately predict personality traits (BIG5) from tens of ‘likes’ or from only a profile picture and a couple of ‘likes’ comparing positively to human reference.

Keywords

Personality Trait Face Image Locally Binary Pattern Profile Image Facial Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported by the ETH General Fund (OK) and by a K40 GPU grant from NVidia. We thank Michal Kosinski and David Stillwell for providing the Facebook BIG5 dataset.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Robert Torfason
    • 1
  • Eirikur Agustsson
    • 1
  • Rasmus Rothe
    • 1
  • Radu Timofte
    • 1
    Email author
  1. 1.Computer Vision LaboratoryETH ZurichZurichSwitzerland

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