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Facial landmarking is a fundamental step in machine-based face analysis. The majority of existing techniques handle such an issue based on 2D images; however, they suffer from illumination and pose variations that largely degrade landmarking performance. The emergence of 3D data provides us with an alternative to overcome these unsolved problems in the 2D domain. This paper proposes a novel approach...
Ethnicity is a key demographic attribute of human beings and it plays a important role in automatic machine based face analysis, therefore, there has been increasing attention for face based ethnicity classification in recent years. In this paper, we propose a novel method on such an issue by combining both boosted local texture and shape features extracted from 3D face models, in contrast to the...
Nose tip localization is often the basic step for 2.5D face registration and further 3D face processing and as such appears as a side problem of most research works on 2.5D or 3D face recognition. In this paper, we propose to carry out a comprehensive study of four popular rotation invariant differential geometric properties, namely Mean and Gaussian curvature, Shape Index and Curvedness, for the...
3D face landmarking aims at automatic localization of 3D facial features and has a wide range of applications, including face recognition, face tracking, facial expression analysis. Methods so far developed for pure 2D texture images were shown sensitive to lighting condition changes. In this paper, we present a statistical model-based technique for accurate 3D face landmarking, thus using an ??analysis...
Automatic 2.5D face landmarking aims at locating facial feature points on 2.5D face models, such as eye corners, nose tip, etc. and has many applications ranging from face registration to facial expression recognition. In this paper, we propose a rotation invariant 2.5D face landmarking solution based on facial curvature analysis combined with a generic 2.5D face model and make use of a coarse-to-fine...
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