TITLE: Evaluation of Human and Machine Face Detection using a Novel Distinctive Human Appearance Dataset 

ABSTRACT: Face detection is a long-standing challenge in the field of computer vision,
with the ultimate goal being to accurately localize human faces in an
unconstrained environment. There are significant technical hurdles in making
these systems accurate due to confounding factors related to pose, image
resolution, illumination, occlusion, and viewpoint [44]. That being said, with
recent developments in machine learning, face-detection systems have achieved
extraordinary accuracy, largely built on data-driven deep-learning models [70].
Though encouraging, a critical aspect that limits face-detection performance
and social responsibility of deployed systems is the inherent diversity of
human appearance. Every human appearance reflects something unique about a
person, including their heritage, identity, experiences, and visible
manifestations of self-expression. However, there are questions about how well
face-detection systems perform when faced with varying face size and shape,
skin color, body modification, and body ornamentation. Towards this goal, we
collected the Distinctive Human Appearance dataset, an image set that
represents appearances with low frequency and that tend to be undersampled in
face datasets. Then, we evaluated current state-of-the-art face-detection
models in their ability to detect faces in these images. The evaluation results
show that face-detection algorithms do not generalize well to these diverse
appearances. Evaluating and characterizing the state of current face-detection
models will accelerate research and development towards creating fairer and
more accurate face-detection systems.
Face detection is a long-standing challenge in the field of computer vision,
with the ultimate goal being to accurately localize human faces in an
unconstrained environment. There are significant technical hurdles in making
these systems accurate due to confounding factors related to pose, image
resolution, illumination, occlusion, and viewpoint [44]. That being said, with
recent developments in machine learning, face-detection systems have achieved
extraordinary accuracy, largely built on data-driven deep-learning models [70].
Though encouraging, a critical aspect that limits face-detection performance
and social responsibility of deployed systems is the inherent diversity of
human appearance. Every human appearance reflects something unique about a
person, including their heritage, identity, experiences, and visible
manifestations of self-expression. However, there are questions about how well
face-detection systems perform when faced with varying face size and shape,
skin color, body modification, and body ornamentation. Towards this goal, we
collected the Distinctive Human Appearance dataset, an image set that
represents appearances with low frequency and that tend to be undersampled in
face datasets. Then, we evaluated current state-of-the-art face-detection
models in their ability to detect faces in these images. The evaluation results
show that face-detection algorithms do not generalize well to these diverse
appearances. Evaluating and characterizing the state of current face-detection
models will accelerate research and development towards creating fairer and
more accurate face-detection systems.
Evaluation of Human and Machine Face Detection using a Novel Distinctive Human Appearance Dataset
http://arxiv.org/abs/2111.00660v2