“A Feature Selection Technique with Genetic Algorithm to Reduce the Visibility of Gradient-based Image Features”

Akira Suzuki, Shingo Ando, Masashi Morimoto, Hideki Koike, Shunichi Yonemura, Katsuyoshi Tanabe

研究成果: Article

抜粋

This paper proposes a feature selection technique with genetic algorithm that reduces the risk of data leaks by reducing the visibility of gradient-based image features. Gradient-based image features, which are used in image classification, are capable of wide application and offer high classification accuracy. However, people can picture the original image in their minds easily from the features when used in high resolution because they are represent appearance. This creates privacy concerns when they are applied to face image recognition. To overcome this problem, we introduce a feature selection technique that uses the genetic algorithm to reduce the visibility of gradient-based image features without sacrificing the recognition rate significantly. To evaluate the performance of the proposed technology, we make an experimental feature selection system that incorporates gender classification software. An experiment shows that the proposed technology can well reduce the visibility of gradient-based image features without sacrificing the recognition rate.

元の言語English
ページ(範囲)448-458
ページ数11
ジャーナルJournal of the Institute of Image Electronics Engineers of Japan
40
発行部数3
DOI
出版物ステータスPublished - 2011
外部発表Yes

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering

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