  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.
