Cartoon Face Recognition: A Benchmark Dataset

Yi Zheng1
Yifan Zhao2
Mengyuan Ren1
He Yan1
Xiangju Lu1*
Junhui Liu1
Jia Li2*
CVTEAM

1iQIYI,Inc

2State Key Laboratory of Virtual Reality Technology and Systems, SCSE, Beihang University

ACM MM 2020

Paper

Code

The overview of dataset

Abstract

Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications. As the first step to understand this media, cartoon face recognition is a crucial but less-explored task with few datasets proposed. In this work, we first present a new challenging benchmark dataset, consisting of 389,678 images of 5,013 cartoon characters annotated with identity, bounding box, pose, and other auxiliary attributes. The dataset, named iCartoonFace, is currently the largest-scale, high-quality, richannotated, and spanning multiple occurrences in the field of image recognition, including near-duplications, occlusions, and appearance changes. In addition, we provide two types of annotations for cartoon media, i.e., face recognition, and face detection, with the help of a semi-automatic labeling algorithm. To further investigate this challenging dataset, we propose a multi-task domain adaptation approach that jointly utilizes the human and cartoon domain knowledge with three discriminative regularizations. We hence perform a benchmark analysis of the proposed dataset and verify the superiority of the proposed approach in the cartoon face recognition task. We believe this public availability will attract more research attention in broad practical application scenarios.

Dataset comparison

Semi-automic assembling process

Overview of proposed approach

BibTex Citation 

@misc{zheng2020cartoon,
title={Cartoon Face Recognition: A Benchmark Dataset}, 
author={Yi Zheng and Yifan Zhao and Mengyuan Ren and He Yan and Xiangju Lu and Junhui Liu and Jia Li},
year={2020},
eprint={1907.13394},
archivePrefix={arXiv},
primaryClass={cs.CV}
}