Mining Hard Augmented Samples for Robust Facial Landmark Localization With CNNs | Zendy

Zhenhua Feng | Zendy; Josef Kittler | Zendy; XiaoJun Wu | Zendy

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Mining Hard Augmented Samples for Robust Facial Landmark Localization With CNNs

Author(s) -

Zhenhua Feng,

Josef Kittler,

XiaoJun Wu

Publication year - 2019

Publication title -

ieee signal processing letters

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.815

H-Index - 138

eISSN - 1558-2361

pISSN - 1070-9908

DOI - 10.1109/lsp.2019.2895291

Subject(s) - landmark , computer science , convolutional neural network , artificial intelligence , pattern recognition (psychology) , boosting (machine learning) , training set , regression , machine learning , mathematics , statistics

Effective data augmentation is crucial for facial landmark localization 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 localization systems. To the best of our knowledge, this is the first study that provides a systematic analysis of different data a...

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