&lt;title&gt;Automatic landmark extraction from three-dimensional head scan data&lt;/title&gt;

Corner, Brian D.; Paquette, Steven

doi:10.1117/12.460170

Cited by 10 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the search for the symmetry axis can be costly without the guiding landmarks. Curvature-based features seem to be promising in 3D due to their invariance to several transformations [53,24]. Especially, Gaussian and mean curvatures are frequently used to locate and segment facial parts.…”

Section: Automatic Landmarkingmentioning

confidence: 99%

“…The most frequently used approach to facial landmark detection is to devise a number of heuristics that seem to work for the experimental conditions at hand [9,16,45,53,99,101]. These can be simple rules, such as taking the point closest to the camera as the tip of the nose [24,102], or using contrast differences to detect eye regions [54,102].…”

Section: Automatic Landmarkingmentioning

confidence: 99%

See 1 more Smart Citation

3D Face Recognition: Technology and Applications

Gökberk

Salah

Alyüz

et al. 2009

Advances in Pattern Recognition

View full text Add to dashboard Cite

Section: Automatic Landmarkingmentioning

confidence: 99%

Section: Automatic Landmarkingmentioning

confidence: 99%

3D Face Recognition: Technology and Applications

Gökberk

Salah

Alyüz

et al. 2009

Advances in Pattern Recognition

View full text Add to dashboard Cite

“…However, the search for the symmetry axis can be costly without the guiding landmarks. Curvature-based features are promising in 3D, but they suffer from a number of problems [9], [10]. Reliable estimation of curvature requires a strong pre-processing that eliminates surface irregularities, especially near eye and mouth corners.…”

mentioning

confidence: 99%

3D Facial Landmarking under Expression, Pose, and Occlusion Variations

Dibeklioğlu

Salah

Akarun

2008

2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems

View full text Add to dashboard Cite

Abstract-Automatic localization of 3D facial features is important for face recognition, tracking, modeling and expression analysis. Methods developed for 2D images were shown to have problems working across databases acquired with different illumination conditions. Expression variations, pose variations and occlusions also hamper accurate detection of landmarks. In this paper we assess a fully automatic 3D facial landmarking algorithm that relies on accurate statistical modeling of facial features. This algorithm can be employed to model any facial landmark, provided that the facial poses present in the training and test conditions are similar. We test this algorithm on the recently acquired Bosphorus 3D face database, and also inspect cross-database performance by using the FRGC database. Then, a curvature-based method for localizing the nose tip is introduced and shown to perform well under severe conditions.

show abstract

“…The shape properties from the maps z u and z v do not uniquely identify the nose tip [11], however, the addition of the radial symmetry map eliminates the majority of false alarms due to data noise and other protruding features that do not share the radial symmetry of the nose tip. Note that in our experiments, only one possible nose region was detected in all but a few cases.…”

Section: Nose Extractionmentioning

confidence: 98%

“…In [11], a number of head and face landmarks are defined in terms of their surface geometry and facial location. Gaussian and mean curvature, as well as partial derivatives of the range images are computed and used to classify critical points on the surface (e.g., peaks, ridges, pits, valleys).…”

Section: Related Workmentioning

confidence: 99%

A Prescreener for 3D Face Recognition Using Radial Symmetry and the Hausdorff Fraction

Koudelka

Koch

Russ

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops

View full text Add to dashboard Cite

Face recognition systems require the ability to efficiently scan an existing database of faces to locate a match for a newly acquired face. The large number of faces in real world databases makes computationally intensive algorithms impractical for scanning entire databases. We propose the use of more efficient algorithms to "prescreen" face databases, determining a limited set of likely matches that can be processed further to identify a match. We use both radial symmetry and shape to extract five features of interest on 3D range images of faces. These facial features determine a very small subset of discriminating points which serve as input to a prescreening algorithm based on a Hausdorff fraction. We show how to compute the Haudorff fraction in linear O(n) time using a range image representation. Our feature extraction and prescreening algorithms are verified using the FRGC v1.0 3D face scan data. Results show 97% of the extracted facial features are within 10 mm or less of manually marked ground truth, and the prescreener has a rank 6 recognition rate of 100%.

show abstract

<title>Automatic landmark extraction from three-dimensional head scan data</title>

Cited by 10 publications

References 0 publications

3D Face Recognition: Technology and Applications

3D Face Recognition: Technology and Applications

3D Facial Landmarking under Expression, Pose, and Occlusion Variations

A Prescreener for 3D Face Recognition Using Radial Symmetry and the Hausdorff Fraction

Contact Info

Product

Resources

About