Precise Localization of Landmarks on 3D Faces using Gabor Wavelets

D'Hose, Johan; Colineau, Joseph; Bichon, C.; Dorizzi, Bernadette

doi:10.1109/btas.2007.4401927

Cited by 34 publications

(22 citation statements)

References 11 publications

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“…The most widely used feature to encode the facial geometry for landmark detection has been surface curvature [1,4,5]. Other geometric features include relief curves [6], the response of range data when convolved with a set of primitive filters [7] or Gabor wavelets [8].…”

Section: Related Workmentioning

confidence: 99%

3D Facial Landmark Localization Using Combinatorial Search and Shape Regression

Sukno

Waddington

Whelan

2012

Computer Vision – ECCV 2012. Workshops and Demonstrations

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Abstract. This paper presents a method for the automatic detection of facial landmarks. The algorithm receives a set of 3D candidate points for each landmark (e.g. from a feature detector) and performs combinatorial search constrained by a deformable shape model. A key assumption of our approach is that for some landmarks there might not be an accurate candidate in the input set. This is tackled by detecting partial subsets of landmarks and inferring those that are missing so that the probability of the deformable model is maximized. The ability of the model to work with incomplete information makes it possible to limit the number of candidates that need to be retained, substantially reducing the number of possible combinations to be tested with respect to the alternative of trying to always detect the complete set of landmarks. We demonstrate the accuracy of the proposed method in a set of 144 facial scans acquired by means of a hand-held laser scanner in the context of clinical craniofacial dysmorphology research. Using spin images to describe the geometry and targeting 11 facial landmarks, we obtain an average error below 3 mm, which compares favorably with other state of the art approaches based on geometric descriptors.

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Section: Related Workmentioning

confidence: 99%

3D Facial Landmark Localization Using Combinatorial Search and Shape Regression

Sukno

Waddington

Whelan

2012

Computer Vision – ECCV 2012. Workshops and Demonstrations

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“…Approaches not based on curvature but still using exclusively 3D geometry as input data, include the response of range data when convolved with a set of primitive filters [29] or Gabor wavelets [4] and combinations of features like spin images, distance to local plane or RBF Shape Histograms [16], [20]. Nonetheless, they do not seem to outperform curvaturebased approaches.…”

Section: A Related Workmentioning

confidence: 99%

A Quantitative Assessment of 3D Facial Key Point Localization Fitting 2D Shape Models to Curvature Information

Sukno

Chowdhury

Waddington

et al. 2011

2011 Irish Machine Vision and Image Processing Conference

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Abstract-This work addresses the localization of 11 prominent facial landmarks in 3D by fitting state of the art shape models to 2D data. Quantitative results are provided for 34 scans at high resolution (texture maps of 10 M-pixels) in terms of accuracy (with respect to manual measurements) and precision (repeatability on different images from the same individual). We obtain an average accuracy of approximately 3 mm, and median repeatability of inter-landmark distances typically below 2 mm, which are values comparable to current algorithms on automatic localization of facial landmarks. We also show that, in our experiments, the replacement of texture information by curvature features produced little change in performance, which is an important finding as it suggests the applicability of the method to any type of 3D data.

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“…Database-dependent hypotheses are sometimes made, for example that the tip of the nose coordinate has a high value along axis z [2] [6], or an extremal value along x when large yaw rotation is present [4]. Most of the methods use, at some point, the notion of local curvature of the surface.…”

Section: Related Workmentioning

confidence: 99%

3D face landmark labelling

Creusot

Pears

Austin

2010

Proceedings of the ACM Workshop on 3D Object Retrieval

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Most 3D face processing systems require feature detection and localisation, for example to crop, register, analyse or recognise faces. The three features often used in the literature are the tip of the nose, and the two inner corner of the eyes. Failure to localise these landmarks can cause the system to fail and they become very difficult to detect under large pose variation or when occlusion is present. In this paper, we present a proof-of-concept for a face labelling system, capable of overcoming this problem, as a larger number of landmarks are employed. A set of points containing handplaced landmarks is used as input data. The aim here is to retrieve the landmark's labels when some part of the face is missing. By using graph matching techniques to reduce the number of candidates, and translation and unit-quaternion clustering to determine a final correspondence, we evaluate the accuracy at which landmarks can be retrieved under changes in expression, orientation and in the presence of occlusions.

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Precise Localization of Landmarks on 3D Faces using Gabor Wavelets

Cited by 34 publications

References 11 publications

3D Facial Landmark Localization Using Combinatorial Search and Shape Regression

3D Facial Landmark Localization Using Combinatorial Search and Shape Regression

A Quantitative Assessment of 3D Facial Key Point Localization Fitting 2D Shape Models to Curvature Information

3D face landmark labelling

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