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Network Structure for Personalized Face-Pose Estimation Using Incrementally Updated Face-Shape Parameters

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Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12047))

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Abstract

This paper proposes a deep learning method for face-pose estimation with an incremental personalization mechanism to update the face-shape parameters. Recent advances in machine learning technology have also led to outstanding performance in applications of computer vision. However, network-based algorithms generally rely on an off-line training process that uses a large dataset, and a trained network (e.g., one for face-pose estimation) usually works in a one-shot manner, i.e., each input image is processed one by one with a static network. On the other hand, we expect a great advantage from having sequential observations, rather than just single-image observations, in many practical applications. In such cases, the dynamic use of multiple observations will contribute to improving system performance. The face-pose estimation method proposed in this paper, therefore, focuses on an incremental personalization mechanism. The method consists of two parts: a pose-estimation network and an incremental estimation of the face-shape parameters (shape-estimation network). Face poses are estimated from input images and face-shape parameters through the pose-estimation network. The shape parameters are estimated as the output of the shape-estimation network and iteratively updated in a sequence of image observations. Experimental results suggest the effectiveness of using face-shape parameters in face-posture estimation. We also describe the incremental refinement of face-shape parameters using a shape-estimation network.

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Acknowledgement

This work was supported by JSPS KAKENHI Grant Number JP18H03269.

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Authors and Affiliations

  1. ATR IRC Labs, Seika-cho, Sorakugun, Kyoto, 619-0288, Japan

    Makoto Sei & Akira Utsumi

  2. Ritsumeikan University, Kusatsu, Shiga, 525-8577, Japan

    Makoto Sei, Hirotake Yamazoe & Joo-Ho Lee

Authors
  1. Makoto Sei
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  2. Akira Utsumi
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  3. Hirotake Yamazoe
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  4. Joo-Ho Lee
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Corresponding author

Correspondence to Makoto Sei .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Sei, M., Utsumi, A., Yamazoe, H., Lee, JH. (2020). Network Structure for Personalized Face-Pose Estimation Using Incrementally Updated Face-Shape Parameters. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-41299-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41298-2

  • Online ISBN: 978-3-030-41299-9

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