Biometric Identification from Human Aesthetic Preferences.

Brandon Sieu,Marina Gavrilova

doi:10.3390/s20041133

Abstract

In recent years, human–machine interactions encompass many avenues of life, ranging from personal communications to professional activities. This trend has allowed for person identification based on behavior rather than physical traits to emerge as a growing research domain, which spans areas such as online education, e-commerce, e-communication, and biometric security. The expression of opinions is an example of online behavior that is commonly shared through the liking of online images. Visual aesthetic is a behavioral biometric that involves using a person’s sense of fondness for images. The identification of individuals using their visual aesthetic values as discriminatory features is an emerging domain of research. This paper introduces a novel method for aesthetic feature dimensionality reduction using gene expression programming. The proposed system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40,000 images demonstrate a 95% accuracy of identity recognition based solely on users’ aesthetic preferences.

Highlights

Human–machine interactions rely on human behavior [1]
While the majority of biometric research focuses on behavioral biometrics such as voice and gait [2], as well as the enhancement of accuracy through information fusion [3,4], this article presents the most comprehensive study to date on the use of aesthetic-based human traits expressed through human–machine interaction for biometric identification
This article presents the most comprehensive study to date on the use of aesthetic-based human traits expressed through human–machine interaction for biometric identification

Summary

Introduction

Human–machine interactions rely on human behavior [1]. Behavioral biometrics can prove effective in situations where a person’s mood, emotions, or intent are to be identified. While the majority of biometric research focuses on behavioral biometrics such as voice and gait [2], as well as the enhancement of accuracy through information fusion [3,4], this article presents the most comprehensive study to date on the use of aesthetic-based human traits expressed through human–machine interaction for biometric identification. With knowledge of an individual’s visual preference from a selection of images, corresponding features can be retrieved, which forms the person’s specific visual aesthetic authentication template. This is the basis of visual aesthetic identification. The present research is a significantly extended version of conference paper [12]

Methods

Results

Conclusion