Head Posture Estimation by Deep Learning Using 3-D Point Cloud Data from a Depth Sensor

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Head posture estimation is performed by capturing characteristic areas of the face, such as the eyes and nose, in images acquired from a camera installed in front of the subject. However, with this method, parts of the eyes and nose are hidden when the subject turns away and faces the side, making estimation difficult. In this letter, we aim to realize a more effective head estimation method than previous research using 3-D point cloud data from a depth sensor. We pursued the estimation of five head posture classes. In the proposed method, first, the 3-D point cloud data of the postures in the five classes are learned by a deep learning model. Next, the posture of the head is estimated using the model. In this letter, many verification experiments confirmed that the proposed method is very effective for head posture estimation with five posture classes.

Original languageEnglish
Article number9462127
JournalIEEE Sensors Letters
Issue number7
Publication statusPublished - 2021 Jul


  • 3-D point cloud data
  • Sensor signal processing
  • deep learning
  • depth sensor
  • head pose estimation

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


Dive into the research topics of 'Head Posture Estimation by Deep Learning Using 3-D Point Cloud Data from a Depth Sensor'. Together they form a unique fingerprint.

Cite this