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, AnyFace, to ease the annotation of various face datasets.
Paper: AnyFace: A Data-Centric Approach ForInput-Agnostic Face Detection
Instructions for AnyFace demo:
We kindly ask you to use the demo version of the project only for good purposes, not to use it for obscene images, and also to comply with ethical norms. We delete the uploaded image after processing it to preserve your privacy.
If you use this project in your work, we kindly ask you to cite our paper:
@article{Kuzdeuov2022,
author = "Askat Kuzdeuov and Darina Koishigarina and Hüseyin Atakan Varol",
title = "{AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection}",
year = "2022",
month = "12",
url = "https://www.techrxiv.org/articles/preprint/AnyFace_A_Data-Centric_Approach_For_Input-Agnostic_Face_Detection/21656993",
doi = "10.36227/techrxiv.21656993.v1" }