3D partial face matching using local shape descriptors

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 in the keypoints neighborhood using local shape descriptors. Face similarity is evaluated by comparing local shape descriptors across inlier pairs of keypoints that match between probe and gallery scans. The recognition accuracy of the approach is experimented using the Face Recognition Grand Challenge v2.0 data set.

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

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

[3]  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..

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

[5]  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.

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

[7]  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.

[8]  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.

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

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

[11]  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).

[12]  Federico Tombari,et al.  Unique Signatures of Histograms for Local Surface Description , 2010, ECCV.

[13]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[14]  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.

[15]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

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

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

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

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

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

[21]  Jitendra Malik,et al.  Recognizing Objects in Range Data Using Regional Point Descriptors , 2004, ECCV.

[22]  Marcel Körtgen,et al.  3D Shape Matching with 3D Shape Contexts , 2003 .