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dc.contributor.authorLiang, A.
dc.contributor.authorWang, C.
dc.contributor.authorLiu, Wan-Quan
dc.contributor.authorLi, L.
dc.identifier.citationLiang, A. and Wang, C. and Liu, W. and Li, L. 2016. Robust and flexible landmarks detection for uncontrolled frontal faces in the wild. Numerical Algebra, Control and Optimization. 6 (3): pp. 263-296.

In this paper, we propose a robust facial landmarking scheme for frontal faces which can be applied on both controlled and uncontrolled environ-ment. This scheme is based on improvement/extension of the tree-structured facial landmarking scheme proposed by Zhu and Ramanan. The whole system is divided into two main parts: face detection and face landmarking. In the face detection part, we proposed a Tree-structured Filter Model (TFM) combined with Viola and Jones face detector to significantly reduce the false positives while maintaining high accuracy. For the facial landmarking step, we improve the accuracy and the amount of the facial landmarks by readjusting the face structure to provide better geometrical information. Furthermore, we expand the face models into Multi-Resolution (MR) models with the adaptive land-mark approach via landmark reduction to train the face models to be able to detect facial landmarks on face images with resolutions as low as 30x30 pixels. Our experiments show that our proposed approaches can improve the accuracy of facial landmark detection on both controlled and uncontrolled environment. Furthermore, they also show that our MR models are more robust on detecting facial components (eyebrows, eyes, nose, and mouth) on very small faces.

dc.publisherAmerican Institute of Mathematical Sciences
dc.titleRobust and flexible landmarks detection for uncontrolled frontal faces in the wild
dc.typeJournal Article
dcterms.source.titleNumerical Algebra, Control and Optimization
curtin.departmentDepartment of Computing
curtin.accessStatusOpen access via publisher

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