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Modified Stacked Hourglass Networks for Facial Landmarks Detection

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Intelligent Information and Database Systems (ACIIDS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11432))

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Abstract

Facial landmarks detection is a fundamental research topic in computer vision. This topic has been largely improved recently thanks to the development of convolution neural networks (CNN). This paper proposes a modified version of the Stacked Hourglass Network, which is a state-of-the-art architecture for landmark localization. Instead of using the original residual block, this paper uses the \(\lambda \)-residual-block to get more effective features. The proposed network can achieve better result than other state-of-the-art methods on two very challenging 3D facial landmark datasets, Menpo-3D and 300 W.

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Authors and Affiliations

  1. School of Electrical Engineering, University of Ulsan, Ulsan, Korea

    Van-Thanh Hoang & Kang-Hyun Jo

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  1. Van-Thanh Hoang
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  2. Kang-Hyun Jo
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Correspondence to Kang-Hyun Jo .

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Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. Bina Nusantara University, Jakarta, Indonesia

    Ford Lumban Gaol

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Hoang, VT., Jo, KH. (2019). Modified Stacked Hourglass Networks for Facial Landmarks Detection. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_56

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  • DOI: https://doi.org/10.1007/978-3-030-14802-7_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14801-0

  • Online ISBN: 978-3-030-14802-7

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