Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Confidence of Statistical Model Based Shape Prediction
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Landmark Localisation in 3D Face Data
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
3-D face detection, landmark localization, and registration using a point distribution model
IEEE Transactions on Multimedia
A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Automatic face segmentation and facial landmark detection in range images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Anthropometric 3D Face Recognition
International Journal of Computer Vision
Proceedings of the ACM workshop on 3D object retrieval
Robust 3D face recognition based on resolution invariant features
Pattern Recognition Letters
Linear Scale and Rotation Invariant Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal landmark detection using shape models and branch and bound
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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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.