Mining Hard Augmented Samples for Robust Facial Landmark Localization With CNNs

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 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.