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Rotation Invariant Face Detection Using HOG and Polar Coordinate Transform

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(11), pp.85-92
  • DOI : 10.9708/jksci.2021.26.11.085
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 12, 2021
  • Accepted : November 18, 2021
  • Published : November 30, 2021

Kyung-Shik Jang 1

1동의대학교

Accredited

ABSTRACT

In this paper, a method for effectively detecting rotated face and rotation angle regardless of the rotation angle is proposed. Rotated face detection is a challenging task, due to the large variation in facial appearance. In the proposed polar coordinate transformation, the spatial information of the facial components is maintained regardless of the rotation angle, so there is no variation in facial appearance due to rotation. Accordingly, features such as HOG, which are used for frontal face detection without rotation but have rotation-sensitive characteristics, can be effectively used in detecting rotated face. Only the training data in the frontal face is needed. The HOG feature obtained from the polar coordinate transformed images is learned using SVM and rotated faces are detected. Experiments on 3600 rotated face images show a rotation angle detection rate of 97.94%. Furthermore, the positions and rotation angles of the rotated faces are accurately detected from images with a background including multiple rotated faces.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.