  Lip segmentation is crucial in computer vision, especially for lip reading.
Despite extensive face segmentation research, lip segmentation has received
limited attention. The aim of this study is to compare state-of-the-art lip
segmentation models using a standardized setting and a publicly available
dataset. Five techniques, namely EHANet, Mask2Former, BiSeNet V2, PIDNet, and
STDC1, are qualitatively selected based on their reported performance,
inference time, code availability, recency, and popularity. The CelebAMask-HQ
dataset, comprising manually annotated face images, is used to fairly assess
the lip segmentation performance of the selected models. Inference experiments
are conducted on a Raspberry Pi4 to emulate limited computational resources.
The results show that Mask2Former and EHANet have the best performances in
terms of mIoU score. BiSeNet V2 demonstrate competitive performance, while
PIDNet excels in recall but has lower precision. Most models present inference
time ranging from 1000 to around 3000 milliseconds on a Raspberry Pi4, with
PIDNet having the lowest mean inference time. This study provides a
comprehensive evaluation of lip segmentation models, highlighting their
performance and inference times. The findings contribute to the development of
lightweight techniques and establish benchmarks for future advances in lip
segmentation, especially in IoT and edge computing scenarios.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning