3D Facial Landmark Detection under Large Yaw and Expression Variations

Perakis, Panagiotis; Passalis, G.; Theoharis, T.; Kakadiaris, Ioannis A.

doi:10.1109/tpami.2012.247

Cited by 104 publications

(80 citation statements)

References 26 publications

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“…Similarly, our method requires accurate and automatic landmark detection. For faces, [10] have shown that this is indeed achievable for 3D data. Finally, capture of a neutral face, while a manual process, is a trivial procedure performed only once per subject.…”

Section: Resultsmentioning

confidence: 90%

Improving 3D Facial Action Unit Detection with Intrinsic Normalization

Yudin¹,

Wetzler²,

Sela³

et al. 2015

Procedings of the Proceedings of the 1st International Workshop on DIFFerential Geometry in Computer Vision for Analysis of Sha

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Data normalization techniques are commonly used to reduce intra-class variance while preserving inter-class differences that aid in classification and regression tasks. Such practices bring all data examples to similar scale and range, and help to decrease the dimensionality of categorization problems. In this paper we describe a novel use of a geometric framework to extend the concept of data normalization to the domain of functions that lie on surfaces. In this context, normalization is posed as an embedding of all example functions into Riemannian manifolds nearly isometric to one another. Using geometric tools, we propose an implementation for the case of discretized functions on triangulated meshes. To demonstrate the proposed framework we apply it as a preprocessing step to the task of automatic facial action unit detection for depth maps from a 3D scanner. We show how this subject-independent representation produces more accurate action unit classification results. We also empirically demonstrate that utilizing this intrinsic normalization technique indeed improves action unit recognition performance.

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Section: Resultsmentioning

confidence: 90%

Improving 3D Facial Action Unit Detection with Intrinsic Normalization

Yudin¹,

Wetzler²,

Sela³

et al. 2015

Procedings of the Proceedings of the 1st International Workshop on DIFFerential Geometry in Computer Vision for Analysis of Sha

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show abstract

“…Perakis et al [11], proposes a 3D landmark detection method for 3D facial scans. It based on Facial Landmark detector Model.…”

Section: Related Work:-mentioning

confidence: 99%

Scrambling Face Images for Privacy Protection Using Arnold Transform.

Abirami¹

2017

IJAR

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“…However, the PDM is sensitive to variations in expression and to incomplete coverage of the 3D face. Afterward, Perakis et al [10] introduced a 3D facial landmark localization method that was robust to yaw and expression changes. In [10], the shape index and spin image are used as features for detecting candidate landmarks e.g., eye corners, mouth corners, and nose tip.…”

Section: B Statistical Point Distribution Model-based Approachmentioning

confidence: 99%

Robust 3D Face Landmark Localization Based on Local Coordinate Coding

Song

Tao

Sun

et al. 2014

IEEE Trans. on Image Process.

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Abstract-In the 3D facial animation and synthesis community, input faces are usually required to be labeled by a set of landmarks for parameterization. Because of the variations in pose, expression and resolution, automatic 3D face landmark localization remains a challenge. In this paper, a novel landmark localization approach is presented. The approach is based on Local Coordinate Coding (LCC) and consists of two stages. In the first stage, we perform nose detection, relying on the fact that the nose shape is usually invariant under the variations in the pose, expression and resolution. Then, we use the Iterative Closest Points (ICP) algorithm to find a 3D affine transformation that aligns the input face to a reference face. In the second stage, we perform re-sampling to build correspondences between the input 3D face and the training faces. Then, an LCC-based localization algorithm is proposed to obtain the positions of the landmarks in the input face. Experimental results show that the proposed method is comparable to state of the art methods in terms of its robustness, flexibility and accuracy.

show abstract

3D Facial Landmark Detection under Large Yaw and Expression Variations

Cited by 104 publications

References 26 publications

Improving 3D Facial Action Unit Detection with Intrinsic Normalization

Improving 3D Facial Action Unit Detection with Intrinsic Normalization

Scrambling Face Images for Privacy Protection Using Arnold Transform.

Robust 3D Face Landmark Localization Based on Local Coordinate Coding

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