Title
Evaluating Spatial Configuration Constrained CNNs for Localizing Facial and Body Pose Landmarks
Abstract
Landmark localization is a widely used task required in medical image analysis and computer vision applications. Formulated in a heatmap regression framework, we have recently proposed a CNN architecture that learns on its own to split the localization task into two simpler sub-problems, dedicating one component to locally accurate but ambiguous predictions, while the other component improves robustness by incorporating the spatial configuration of landmarks to remove ambiguities. We learn this simplification in our SpatialConfiguration-Net (SCN) by multiplying the heatmap predictions of its two components and by training the network in and end-to-end manner, thus achieving regularization similar to e.g. a hand-crafted Markov Random Field model. While we have previously shown localization results solely on data from 2D and 3D medical imaging modalities, in this work our aim is to study the generalization capabilities of our SpatialConfiguration-Net to computer vision problems. Therefore, we evaluate our performance both in terms of accuracy and robustness on a facial alignment task, where we improve upon the state-of-the-art methods, as well as on a human body pose estimation task, where we demonstrate results in line with the recent state-of-the-art.
Year
DOI
Venue
2019
10.1109/IVCNZ48456.2019.8961000
2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)
Keywords
Field
DocType
anatomical landmark localization,convolutional neural network,facial image analysis,human pose estimation
Modalities,Computer vision,Pattern recognition,Medical imaging,Convolutional neural network,Markov random field,Computer science,Pose,Robustness (computer science),Regularization (mathematics),Artificial intelligence,Landmark
Conference
ISSN
ISBN
Citations 
2151-2191
978-1-7281-4188-6
0
PageRank 
References 
Authors
0.34
23
3
Name
Order
Citations
PageRank
Christian Payer1414.73
Darko Stern200.34
Martin Urschler334723.94