Abstract
Face detection is a mandatory step in many computer vision applications,
such as face recognition, emotion recognition, age detection, virtual
makeup, and vital sign monitoring. Thanks to advancements in deep
learning and the introduction of annotated large-scale datasets,
numerous applications have been developed for human faces. Recently,
other domains, such as animals and cartoon characters, have started
gaining attention but still lag far behind human faces. The biggest
challenge is the limited number of annotated face datasets in these
domains. The manual labeling of large-scale datasets is tedious and
requires substantial human labor. In this regard, we present an
input-agnostic face detector to ease the annotation of various face
datasets. We propose a simple but effective data-centric approach
instead of building a specific neural network architecture.
Specifically, we trained a face detection model, YOLO5Face, on human,
animal, and cartoon face datasets. The experiments show that the model
can achieve accurate results in all domains. In addition, the model
achieved decent results for animals and cartoon characters different
from the ones in the training set. This implies that the model can
extract agnostic facial features. We have made the source code and
pre-trained models publicly available at
https://github.com/IS2AI/AnyFace to stimulate research in these fields.