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International Conference on Engineering Applications of Neural Networks

EANN 2017: Engineering Applications of Neural Networks pp 194–204Cite as

Improving Face Pose Estimation Using Long-Term Temporal Averaging for Stochastic Optimization

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 744))

Abstract

Among the most crucial components of an intelligent system capable of assisting drone-based cinematography is estimating the pose of the main actors. However, training deep CNNs towards this task is not straightforward, mainly due to the noisy nature of the data and instabilities that occur during the learning process, significantly slowing down the development of such systems. In this work we propose a temporal averaging technique that is capable of stabilizing as well as speeding up the convergence of stochastic optimization techniques for neural network training. We use two face pose estimation datasets to experimentally verify that the proposed method can improve both the convergence of training algorithms and the accuracy of pose estimation. This also reduces the risk of stopping the training process when a bad descent step was taken and the learning rate was not appropriately set, ensuring that the network will perform well at any point of the training process.

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Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 731667 (MULTIDRONE). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains.

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Nikolaos Passalis & Anastasios Tefas

Authors
  1. Nikolaos Passalis
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  2. Anastasios Tefas
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Corresponding author

Correspondence to Nikolaos Passalis .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

  2. Democritus University of Thrace, University Campus, Xanthi, Greece

    Lazaros Iliadis

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Univesity of Ioannina, Ioannina, Greece

    Aristidis Likas

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Passalis, N., Tefas, A. (2017). Improving Face Pose Estimation Using Long-Term Temporal Averaging for Stochastic Optimization. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-65172-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65171-2

  • Online ISBN: 978-3-319-65172-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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