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Constellation-Based Deep Ear Recognition

  • Dejan Štepec
  • Žiga EmeršičEmail author
  • Peter Peer
  • Vitomir Štruc
Chapter
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Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Abstract

This chapter introduces COM-Ear, a deep constellation model for ear recognition. Different from competing solutions, COM-Ear encodes global as well as local characteristics of ear images and generates descriptive ear representations that ensure competitive recognition performance. The model is designed as dual-path convolutional neural network (CNN), where one path processes the input in a holistic manner, and the second captures local images characteristics from image patches sampled from the input image. A novel pooling operation, called patch-relevant-information pooling, is also proposed and integrated into the COM-Ear model. The pooling operation helps to select features from the input patches that are locally important and to focus the attention of the network to image regions that are descriptive and important for representation purposes. The model is trained in an end-to-end manner using a combined cross-entropy and center loss. Extensive experiments on the recently introduced Extended Annotated Web Ears (AWEx).

Keywords

Ear biometrics Ear recognition Part-based models Constellation model Convolutional neural networks 

Notes

Acknowledgements

This research was supported in parts by the ARRS (Slovenian Research Agency) Research Program P2-0250 (B) Meteorology and Biometric Systems, the ARRS Research Program P2-0214 (A) Computer Vision. The authors thank NVIDIA for donating the Titan Xp GPU that was used in the experiments.

This work was also partially supported by the European Commission through the Horizon 2020 research and innovation program under grants 688201 (M2DC) and 690907 (IDENTITY).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Dejan Štepec
    • 1
  • Žiga Emeršič
    • 2
    Email author
  • Peter Peer
    • 2
  • Vitomir Štruc
    • 3
  1. 1.XLAB d.o.o.LjubljanaSlovenia
  2. 2.Computer Vision Laboratory, Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia
  3. 3.Laboratory of Artificial Perception, Systems and Cybernetics, Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia

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