Sense Beyond Expressions: Cuteness

TL;DR

This paper introduces a new dataset and method for analyzing and predicting human cuteness from images, revealing key attributes that influence perceived cuteness.

Contribution

It provides the first comprehensive dataset with annotated cuteness scores and facial attributes, and proposes a novel C-LSVM method for accurate cuteness prediction.

Findings

Identified critical facial attributes influencing cuteness.

Validated the effectiveness of C-LSVM in predicting cuteness scores.

Demonstrated the importance of high-level features in aesthetic perception.

Abstract

With the development of Internet culture, cuteness has become a popular concept. Many people are curious about what factors making a person look cute. However, there is rare research to answer this interesting question. In this work, we construct a dataset of personal images with comprehensively annotated cuteness scores and facial attributes to investigate this high-level concept in depth. Based on this dataset, through an automatic attributes mining process, we find several critical attributes determining the cuteness of a person. We also develop a novel Continuous Latent Support Vector Machine (C-LSVM) method to predict the cuteness score of one person given only his image. Extensive evaluations validate the effectiveness of the proposed method for cuteness prediction.