Sparse Matching of Salient Facial Curves for Recognition of 3-D Faces With Missing Parts

In this work, we propose and experiment a 3-D face recognition approach capable of performing accurate face matching also in the case where just parts of probe scans are available. This is obtained through an original face representation and matching solution that first extracts keypoints of the 3-D depth image of the face and then measures how the face depth changes along facial curves connecting pairs of keypoints. Face similarity is evaluated by sparse comparison of facial curves defined across inlier pairs of matching keypoints between probe and gallery scans. In doing so, a statistical model is also proposed to associate facial curves of the gallery scans with a saliency measure so that curves that model characterizing traits of some subjects are distinguished from curves that are frequently observed in the face of many different subjects. Following recent related work, the recognition accuracy of the approach is experimented using two datasets, both comprising scans with missing parts: the Face Recognition Grand Challenge v2.0 dataset combined with the University of Notre Dame probes; the Gavab dataset.

[1]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[2]  Raimondo Schettini,et al.  Gappy PCA Classification for Occlusion Tolerant 3D Face Detection , 2009, Journal of Mathematical Imaging and Vision.

[3]  J. Foster,et al.  Atlas of Cosmetic Surgery. , 2003 .

[4]  Stefanos Zafeiriou,et al.  Recognition of 3D facial expression dynamics , 2012, Image Vis. Comput..

[5]  Vincent Lepetit,et al.  A fast local descriptor for dense matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Paul Suetens,et al.  Feature detection on 3D face surfaces for pose normalisation and recognition , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[7]  Berk Gökberk,et al.  Regional Registration for Expression Resistant 3-D Face Recognition , 2010, IEEE Transactions on Information Forensics and Security.

[8]  Anil K. Jain,et al.  Multimodal Facial Feature Extraction for Automatic 3D Face Recognition , 2005 .

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

[10]  Arnold W. M. Smeulders,et al.  The Distribution Family of Similarity Distances , 2007, NIPS.

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

[12]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Rong Xiao,et al.  3D Face Recognition by Local Shape Difference Boosting , 2008, ECCV.

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

[15]  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.

[16]  A FischlerMartin,et al.  Random sample consensus , 1981 .

[17]  Xiaoou Tang,et al.  Robust 3D Face Recognition by Local Shape Difference Boosting , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  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..

[19]  Anuj Srivastava,et al.  Shape Analysis of Elastic Curves in Euclidean Spaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Ioannis A. Kakadiaris,et al.  Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Anil K. Jain,et al.  Matching 2.5D face scans to 3D models , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

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

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

[25]  Alberto Del Bimbo,et al.  Superfaces: A Super-Resolution Model for 3D Faces , 2012, ECCV Workshops.

[26]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[27]  Patrick J. Flynn,et al.  Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  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).

[29]  Di Huang,et al.  3-D Face Recognition Using eLBP-Based Facial Description and Local Feature Hybrid Matching , 2012, IEEE Transactions on Information Forensics and Security.

[30]  Alan C. Bovik,et al.  Anthropometric 3D Face Recognition , 2010, International Journal of Computer Vision.

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

[32]  Hassen Drira,et al.  SHREC '11 Track: 3D Face Models Retrieval , 2011, 3DOR@Eurographics.

[33]  Tieniu Tan,et al.  Combining local features for robust nose location in 3D facial data , 2006, Pattern Recognit. Lett..

[34]  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.

[35]  Aly A. Farag,et al.  Surfacing Signatures: An Orientation Independent Free-Form Surface Representation Scheme for the Purpose of Objects Registration and Matching , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[37]  J BeslPaul,et al.  A Method for Registration of 3-D Shapes , 1992 .

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

[39]  Alberto Del Bimbo,et al.  3D partial face matching using local shape descriptors , 2011, J-HGBU '11.

[40]  W. Weibull.  A Statistical Distribution Function of Wide Applicability , 1951 .

[41]  Yunhong Wang,et al.  3D Face recognition using distinctiveness enhanced facial representations and local feature hybrid matching , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

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

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

[44]  Alberto Del Bimbo,et al.  Facial curves between keypoints for recognition of 3D faces with missing parts , 2011, CVPR 2011 WORKSHOPS.

[45]  Alberto Del Bimbo,et al.  The florence 2D/3D hybrid face dataset , 2011, J-HGBU '11.

[46]  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.

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