Gender Classification Using Gaze Distributions for Privacy-Protection of Training Samples
Michiko INOUE, Masashi Nishiyama, Yoshio Iwai · Journal of the Japan Society for Precision Engineering · 2019
We propose a method for classifying gender using training samples after applying privacy-protection. Recently, training samples containing individuals require to protect their privacy. Head regions of training sample are usually manipulated for privacy-protection. However, the accuracy of gender classification is degraded when directly using the protected training samples. Here, we aim to use the human visual capability that people can correctly recognize males and females though the head regions are manipulated. We use gaze distributions of observers who view stimulus images for the preprocessing of gender classifier. Experimental results show that our method improved the accuracy of gender classification after manipulating the training samples by masking, pixelation and blur for privacy-protection.