Facial curves between keypoints for recognition of 3D faces with missing parts

In this work, we propose and experiment an original solution to 3D face recognition that supports accurate face matching also in cases where just some parts of probe scans are available. In the proposed approach, distinguishing traits of the face are captured by first extracting keypoints of the 3D depth image and then measuring how the face depth changes along facial curves connecting pairs of key-points. Face similarity is evaluated by comparing facial curves across inlier pairs of keypoints that match between probe and gallery scans. In doing so, facial curves of the gallery scans are associated with a saliency measure in order to distinguish curves that model characterizing traits of some subjects from curves that are frequently observed in the face of many different subjects. The recognition accuracy of the approach is experimented using the Face Recognition Grand Challenge v2.0 dataset.

[1]  Ryutarou Ohbuchi,et al.  Scale-weighted dense bag of visual features for 3D model retrieval from a partial view 3D model , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[2]  Ioannis A. Kakadiaris,et al.  Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Alberto Del Bimbo,et al.  A Set of Selected SIFT Features for 3D Facial Expression Recognition , 2010, 2010 20th International Conference on Pattern Recognition.

[4]  Arman Savran,et al.  Bosphorus Database for 3D Face Analysis , 2008, BIOID.

[5]  Alberto Del Bimbo,et al.  3D Face Recognition using iso-Geodesic Surfaces , 2007, IRCDL.

[6]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Mohammed Bennamoun,et al.  An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Hassen Drira,et al.  Pose and Expression-Invariant 3D Face Recognition using Elastic Radial Curves , 2010, BMVC.

[9]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[10]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[11]  Alexander M. Bronstein,et al.  Robust Expression-Invariant Face Recognition from Partially Missing Data , 2006, ECCV.

[12]  Mohammed Bennamoun,et al.  Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition , 2007, International Journal of Computer Vision.

[13]  Alberto Del Bimbo,et al.  3D Face Recognition Using Isogeodesic Stripes , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Patrick J. Flynn,et al.  A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition , 2006, Comput. Vis. Image Underst..

[15]  Patrick J. Flynn,et al.  A Region Ensemble for 3-D Face Recognition , 2008, IEEE Transactions on Information Forensics and Security.

[16]  T. Theoharis,et al.  Partial matching of interpose 3D facial data for face recognition , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[17]  Michael Mayo,et al.  3D Face Recognition Using Multiview Keypoint Matching , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[18]  L. Akarun,et al.  A 3D Face Recognition System for Expression and Occlusion Invariance , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.

[19]  Alberto Del Bimbo,et al.  Recognition of 3D faces with missing parts based on profile networks , 2010, 3DOR '10.