WebCaricature: a benchmark for caricature recognition

TL;DR

This paper introduces a new, challenging caricature dataset and evaluation protocols to advance research in caricature recognition, highlighting existing challenges and providing baseline performances for future improvements.

Contribution

The paper presents a comprehensive caricature dataset, evaluation protocols, and a recognition framework, addressing the lack of suitable datasets and analyzing recognition challenges.

Findings

The dataset is more challenging with diverse artistic styles and variations.

Baseline performances reveal significant room for improvement.

Analysis identifies key challenges in caricature recognition.

Abstract

Studying caricature recognition is fundamentally important to understanding of face perception. However, little research has been conducted in the computer vision community, largely due to the shortage of suitable datasets. In this paper, a new caricature dataset is built, with the objective to facilitate research in caricature recognition. All the caricatures and face images were collected from the Web. Compared with two existing datasets, this dataset is much more challenging, with a much greater number of available images, artistic styles and larger intra-personal variations. Evaluation protocols are also offered together with their baseline performances on the dataset to allow fair comparisons. Besides, a framework for caricature face recognition is presented to make a thorough analyze of the challenges of caricature recognition. By analyzing the challenges, the goal is to show problems that worth to be further investigated. Additionally, based on the evaluation protocols and the framework, baseline performances of various state-of-the-art algorithms are provided. A conclusion is that there is still a large space for performance improvement and the analyzed problems still need further investigation.