Abstract
Aesthetic preference can be described as one's taste or fondness for a particular subject. This information has become ubiquitous as online communities and social media have grown increasingly integrated with daily life. The domain of social-behavioral biometrics analyzes the interactions, relations, and communications of individuals rather than traditional physical traits. Recent research has demonstrated that a person's visual aesthetic preferences possess discriminatory value for person identification. This paper introduces the first audio and visual multi-modal aesthetic identification system that utilizes both user-liked images and songs for an accurate identity prediction with score-level fusion. The developed multimodal system achieves an accuracy of 99.4% on the proprietary audio-visual dataset, outperforming unimodal systems.
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