Instantaneous real-time head pose at a distance

Sankha S. Mukherjee, Rolf Baxter, Neil Robertson

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


In this paper we focus on robust, real-time human head pose estimation in low resolution RGB data without any smoothing motion priors e.g. direction of motion. Our main contributions lie in three major areas. First, we show that a generative Deep Belief Network model can be learned on human head data from multiple types of data sources. These sources have similar underlying data that are not necessarily labelled or have the same kind of ground truth. Second, we perform discriminative training using multiple disparate supervisory labels to fine tune the model for head pose estimation. Third, we present state-of-the-art results on two publicly available datasets using this new approach. Our implemetation computes head pose for a head image in 0.8 milliseconds, making it real-time and highly scalable.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP)
Subtitle of host publicationProceedings
Number of pages5
ISBN (Print)9781479983391
Publication statusPublished - 2015
Event22nd IEEE International Conference on Image Processing 2015 - Quebec City, Canada
Duration: 27 Sept 201530 Sept 2015
Conference number: 22


Conference22nd IEEE International Conference on Image Processing 2015
Abbreviated titleICIP 2015
CityQuebec City


  • Deep Belief Network
  • Deep Learning
  • Gaze
  • Head Pose
  • Surveillance
  • Unsupervised Learning

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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