AestheticNet: deep convolutional neural network for person identification from visual aesthetic

Abstract

A person’s visual aesthetics is an emerging behavioral biometric. Visual aesthetics can be defined as a person’s principles pertaining to their sense of beauty or fondness. Utilizing a person’s preference to certain images as discriminatory features forms the basis of person identification from visual aesthetics. This paper proposes a novel three-stage framework based on the convolutional neural network, AestheticNet, for the extraction of high-level features and identification of individuals from visual aesthetics. The rank-1 accuracy of 97.73% and rank-5 accuracy of 99.85% are achieved on the publicly available benchmark dataset, which outperforms all state-of-the-art methods.