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Mining Hard Augmented Samples for Robust Facial Landmark Localisation with CNNs

Feng, Zhenhua, Kittler, Josef and Wu, Xiaojun (2019) Mining Hard Augmented Samples for Robust Facial Landmark Localisation with CNNs IEEE Signal Processing Letters, 26 (3). pp. 450-454.

Mining Hard Augmented Samples for Robust Facial Landmark Localisation with CNNs.pdf - Accepted version Manuscript

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Effective data augmentation is crucial for facial landmark localisation with Convolutional Neural Networks (CNNs). In this letter, we investigate different data augmentation techniques that can be used to generate sufficient data for training CNN-based facial landmark localisation systems. To the best of our knowledge, this is the first study that provides a systematic analysis of different data augmentation techniques in the area. In addition, an online Hard Augmented Example Mining (HAEM) strategy is advocated for further performance boosting. We examine the effectiveness of those techniques using a regression-based CNN architecture. The experimental results obtained on the AFLW and COFW datasets demonstrate the importance of data augmentation and the effectiveness of HAEM. The performance achieved using these techniques is superior to the state-of-the-art algorithms.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Wu, Xiaojun
Date : March 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1109/LSP.2019.2895291
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Facial landmark localisation; Deep neural networks; Data augmentation; Hard augmented example mining; Training; Face; Image color analysis; Data mining; Colored noise; Neural networks; Shape
Depositing User : Clive Harris
Date Deposited : 04 Feb 2019 09:24
Last Modified : 24 Jun 2019 07:15

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