Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Rotated Face Detection Using Polar Coordinate Transform and AdaBoost

극좌표계 변환과 AdaBoost를 이용한 회전 얼굴 검출

  • Received : 2021.06.07
  • Accepted : 2021.06.22
  • Published : 2021.07.31
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Abstract

Rotated face detection is required in many applications but still remains as a challenging task, due to the large variations of face appearances. In this paper, a polar coordinate transform that is not affected by rotation is proposed. In addition, a method for effectively detecting rotated faces using the transformed image has been proposed. The proposed polar coordinate transform maintains spatial information between facial components such as eyes, mouth, etc., since the positions of facial components are always maintained regardless of rotation angle, thereby eliminating rotation effects. Polar coordinate transformed images are trained using AdaBoost, which is used for frontal face detection, and rotated faces are detected. We validate the detected faces using LBP that trained the non-face images. Experiments on 3600 face images obtained by rotating images in the BioID database show a rotating face detection rate of 96.17%. Furthermore, we accurately detected rotated faces in images with a background containing multiple rotated faces.

회전된 얼굴 검출은 많은 응용 분야에서 필요하지만 회전에 따른 얼굴 모양의 큰 변화로 인해 여전히 어려운 분야이다. 이 논문에서는 회전의 영향을 받지 않는 극좌표 변환 방법과 변환된 영상을 이용하여 회전얼굴을 효과적으로 검출하는 방법이 제안되었다. 제안한 극좌표계 변환 방법은 회전 각도와 무관하게 눈, 입 등과 같은 얼굴 구성 요소들의 위치가 항상 유지되기 때문에 얼굴 구성요소들 간의 공간 정보가 유지되며, 이로 인해 회전 효과가 제거된다. 극좌표계 변환된 영상을 정면 얼굴 검출에 사용되는 AdaBoost를 이용하여 학습하고 회전 얼굴을 검출하였다. 비얼굴 영상을 LBP를 이용하여 학습하고 검출한 얼굴을 검증하였다. BioID 데이터베이스에 있는 영상을 회전하여 얻은 3600개 얼굴영상에 대한 실험 결과 96.17%의 회전얼굴 검출률을 얻었다. 또한, 다수의 회전 얼굴이 포함된 배경이 있는 영상에서 회전 얼굴들을 정확하게 검출하였다.

Keywords

Acknowledgement

This Work was supported by Dong-eui University Foundation Grant(2018)

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