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Visual Tracking by Local Superpixel Matching with Markov Random Field Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Facial Landmark Localization by Part-Aware Deep Convolutional Network

Authors : Keke He, Xiangyang Xue

Published in: Advances in Multimedia Information Processing - PCM 2016

Publisher: Springer International Publishing

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Abstract

Facial landmark localization is a very challenging research task. The localization accuracy of landmarks on separate facial parts differ greatly due to texture and shape, however most existing methods fail to consider the part location of landmarks. To solve this problem, we propose a novel end-to-end regression framework using deep convolutional neural network (CNN). Our deep architecture first encodes the image into feature maps shared by all the landmarks. Then, these features are sent into two independent sub-network modules to regress contour landmarks and inner landmarks, respectively. Extensive evaluations conducted on 300-W benchmark dataset demonstrate the proposed deep framework achieves state-of-the-art results.

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previous chapter Saliency Detection Combining Multi-layer Integration Algorithm with Background Prior and Energy Function
next chapter On Combining Compressed Sensing and Sparse Representations for Object Tracking
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Metadata
Title
Facial Landmark Localization by Part-Aware Deep Convolutional Network
Authors
Keke He
Xiangyang Xue
Copyright Year
2016
Book
Advances in Multimedia Information Processing - PCM 2016

Print ISBN: 978-3-319-48889-9

Electronic ISBN: 978-3-319-48890-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48890-5_3