Gender Classification using the Gaze Distributions of Observers on Privacy-Protected Training Images

[Abstract]

We propose a method for classifying the gender of pedestrians using a classifier trained by images containing privacy-protection of the head region. Recently, manipulated training images containing pedestrians have been required to protect the privacy of pedestrians. In particular, the head regions of the training images are manipulated. However, the accuracy of gender classification decreases when privacy-protected training images are directly used. To overcome this issue, we aim to use the human visual ability to correctly discriminate males from females even though the head regions have been manipulated. We measure the gaze distributions of observers who view pedestrian images and use them to pre-process gender classifiers. The experimental results show that our method using gaze distribution improved the accuracy of gender classification when the head regions of the training images have been manipulated with masking, pixelization, and blur for privacy-protection.

[Publications]

• Michiko Inoue, Masashi Nishiyama, Yoshio Iwai,
  Gender Classification using the Gaze Distributions of Observers on Privacy-Protected Training Images,
  Proceedings of 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), Vol. 4: VISAPP, pp. 149 - 156, February 2020.
• 井上 路子, 西山 正志, 岩井 儀雄,
  プライバシーが保護された訓練画像に対する視線位置分布を用いた性別認識,
  精密工学会誌, vol.85 , no.12, pp. 1094 - 1101, December 2019.