Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates

  • Alexander Freytag
  • Erik Rodner
  • Marcel Simon
  • Alexander Loos
  • Hjalmar S. Kühl
  • Joachim Denzler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)

Abstract

In this paper, we investigate how to predict attributes of chimpanzees such as identity, age, age group, and gender. We build on convolutional neural networks, which lead to significantly superior results compared with previous state-of-the-art on hand-crafted recognition pipelines. In addition, we show how to further increase discrimination abilities of CNN activations by the Log-Euclidean framework on top of bilinear pooling. We finally introduce two curated datasets consisting of chimpanzee faces with detailed meta-information to stimulate further research. Our results can serve as the foundation for automated large-scale animal monitoring and analysis.

Supplementary material

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Supplementary material 1 (pdf 233 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Alexander Freytag
    • 1
    • 2
  • Erik Rodner
    • 1
    • 2
  • Marcel Simon
    • 1
  • Alexander Loos
    • 3
  • Hjalmar S. Kühl
    • 4
    • 5
  • Joachim Denzler
    • 1
    • 2
    • 5
  1. 1.Computer Vision GroupFriedrich Schiller University JenaJenaGermany
  2. 2.Michael Stifel Center JenaJenaGermany
  3. 3.Fraunhofer Institute for Digital Media TechnologyIlmenauGermany
  4. 4.Max Planck Institute for Evolutionary AnthropologyLeipzigGermany
  5. 5.German Centre for Integrative Biodiversity Research (iDiv)LeipzigGermany

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