Križaj, Janez; Dobrišek, Simon; Mihelič, France; Štruc, Vitomir
Facial Landmark Localization from 3D Images Inproceedings
In: Proceedings of the Electrotechnical and Computer Science Conference (ERK), Portorož, Slovenia, 2016.
A novel method for automatic facial landmark localization is presented. The method builds on the supervised descent framework, which was shown to successfully localize landmarks in the presence of large expression variations and mild occlusions, but struggles when localizing landmarks on faces with large pose variations. We propose an extension of the supervised descent framework which trains multiple descent maps and results in increased robustness to pose variations. The performance of the proposed method is demonstrated on the Bosphorus database for the problem of facial landmark localization from 3D data. Our experimental results show that the proposed method exhibits increased robustness to pose variations, while retaining high performance in the case of expression and occlusion variations.
Camgoz, Necati Cihan; Štruc, Vitomir; Gokberk, Berk; Akarun, Lale; Kindiroglu, Ahmet Alp
In: Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW): Chaa Learn, pp. 136–141, 2015.
Supervised Descent Method (SDM) has proven successful in many computer vision applications such as face alignment, tracking and camera calibration. Recent studies which used SDM, achieved state of the-art performance on facial landmark localization in depth images . In this study, we propose to use ridge regression instead of least squares regression for learning the SDM, and to change feature sizes in each iteration, effectively turning the landmark search into a coarse to fine process. We apply the proposed method to facial landmark localization on the Bosphorus 3D Face Database; using frontal depth images with no occlusion. Experimental results confirm that both ridge regression and using adaptive feature sizes improve the localization accuracy considerably