Abstract
We present a highly accurate and very efficient approach for face alignment, called Extended Robust Cascaded Pose Regression (ERCPR), which is robust to large variations due to differences in expressions and pose. Unlike previous shape regression-based approaches, we propose to reference features weighted by three different face landmarks, which are much more robust to shape variations. Then, a correlation-based feature selection method and a two-level boosted regression are applied to establish accurate relation between features and shapes. Experiments on two challenging face datasets (LFPW, COFW) show that our proposed approach significantly outperforms the state-of-art in terms of both efficiency and accuracy.
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Ge, Y., Ren, X., Peng, C., Wang, X. (2016). Extended Robust Cascaded Pose Regression for Face Alignment. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_6
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DOI: https://doi.org/10.1007/978-3-319-46654-5_6
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