Abstract
The paper proposes a novel Robust Discriminative Regression (RDR) to handle the partial facial landmarks invisible problem for facial landmark localization. RDR consists of multiple partial feature regressors and a regression tree combination strategy to copes with the partial invisible problem together with the optimal multi output combination problem. The RDR is implemented with SIFT features and Linear Regression to achieve the balance of accuracy and computation efficiency. Experiments on two widely used “face in-the-wild” databases (LFPW and COFW) show that the proposed RDR outperforms other state-of-the-art facial landmark localization methods especially in the cases of partial occlusion and large pose variation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
