Feature Extraction with Computational Intelligence for Head Pose Estimation

Research output: Contribution to conferencePaperpeer-review

65 Downloads (Pure)


Measuring social signals has often proved challenging as they are often characterized by subtle movements which are difficult to detect. Head pose is one such social signal used to indicate where an individual’s attention is focused. This paper will discuss the problem of head pose estimation by defining the problem in terms of two fields of view, pan and tilt. A novel approach for head pose estimation is described that uses histogram of oriented gradients with support vector machines. The approach is compared with a template matching approach, among others, using a well-known dataset. The results show that the histogram of oriented gradients approach is the most accurate, able to determine head pan to within one class approximately 79% of the time, and head tilt to within one class approximately 82% of the time.
Original languageEnglish
Number of pages7
Publication statusAccepted/In press - 1 Sept 2018
EventSYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE: IEEE Symposium on Computational Intelligence in Feature Analysis, Selection and Learning in Image and Pattern Recognition - Benguluru, India
Duration: 18 Nov 201820 Nov 2018


Abbreviated titleFASLIP


  • Head Pose estimation
  • Template matching
  • Social signal processing
  • Histogram of oriented gradients
  • Support vector machine


Dive into the research topics of 'Feature Extraction with Computational Intelligence for Head Pose Estimation'. Together they form a unique fingerprint.

Cite this