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

Abstract

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.