14 research outputs found
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
Image landmark detection aims to automatically identify the locations of
predefined fiducial points. Despite recent success in this field,
higher-ordered structural modeling to capture implicit or explicit
relationships among anatomical landmarks has not been adequately exploited. In
this work, we present a new topology-adapting deep graph learning approach for
accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection.
The proposed method constructs graph signals leveraging both local image
features and global shape features. The adaptive graph topology naturally
explores and lands on task-specific structures which are learned end-to-end
with two Graph Convolutional Networks (GCNs). Extensive experiments are
conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as
well as three real-world X-ray medical datasets (Cephalometric (public), Hand
and Pelvis). Quantitative results comparing with the previous state-of-the-art
approaches across all studied datasets indicating the superior performance in
both robustness and accuracy. Qualitative visualizations of the learned graph
topologies demonstrate a physically plausible connectivity laying behind the
landmarks.Comment: Accepted to ECCV-20. Camera-ready with supplementary materia
Py-Feat: Python Facial Expression Analysis Toolbox
Studying facial expressions is a notoriously difficult endeavor. Recent
advances in the field of affective computing have yielded impressive progress
in automatically detecting facial expressions from pictures and videos.
However, much of this work has yet to be widely disseminated in social science
domains such as psychology. Current state of the art models require
considerable domain expertise that is not traditionally incorporated into
social science training programs. Furthermore, there is a notable absence of
user-friendly and open-source software that provides a comprehensive set of
tools and functions that support facial expression research. In this paper, we
introduce Py-Feat, an open-source Python toolbox that provides support for
detecting, preprocessing, analyzing, and visualizing facial expression data.
Py-Feat makes it easy for domain experts to disseminate and benchmark computer
vision models and also for end users to quickly process, analyze, and visualize
face expression data. We hope this platform will facilitate increased use of
facial expression data in human behavior research.Comment: 25 pages, 3 figures, 5 table