Improving head pose estimation with a combined loss and bounding box margin adjustment

Mingzhen Shao, Zhun Sun, Mete Ozay, Takayuki Okatani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in recent works. However, we show that the following two methods, despite their simplicity, can attain further improvement: (i) proper adjustment of the margin of bounding box of a detected face, and (ii) choice of loss functions. We show that the integration of these two methods achieve the new state-of-the-art on standard benchmark datasets for in-the-wild head pose estimation. The Tensorflow implementation of our work is available at https://github.com/MingzhenShao/HeadPose.

Original languageEnglish
Title of host publicationProceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728100890
DOIs
Publication statusPublished - 2019 May
Event14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019 - Lille, France
Duration: 2019 May 142019 May 18

Publication series

NameProceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019

Conference

Conference14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
Country/TerritoryFrance
CityLille
Period19/5/1419/5/18

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Media Technology

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