A geodesic multipolar parameterization-based representation for 3D face recognition

Abstract Here, we propose a new 3D representation designed in the objective of face recognition independently of the expressions. It is defined on localities presenting limited deformations following expression variations. The proposed representation is based on the multipolar parameterization that we recently introduced in Jribi et al. (2019) which is relative invariant under three dimensional Euclidean transformations and robust to the original mesh. A choice of the number of reference points of each multipolar parameterization is made according to the shape of the two types of region of the face namely those of nose and the eyes. The main curvature field is estimated on the parameterizations of each region. The parameters of dimensional reduction algorithms applied to the overall description are adjusted so that the recognition rates remain efficient. The experiments are carried out on three challenging 3D face databases: the FRGC v2.0, the BU-3DFE and Bosphorus. Very high rates are obtained for both identification and verification scenarios. These results are very competitive with state of the art methods.

[1]  Ioannis A. Kakadiaris,et al.  Evaluation of 3D Face Recognition in the presence of facial expressions: an Annotated Deformable Model approach , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[2]  Wei-Yang Lin,et al.  3D Face Recognition Under Expression Variations using Similarity Metrics Fusion , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[3]  Patrick J. Flynn,et al.  Adaptive Rigid Multi-region Selection for Handling Expression Variation in 3D Face Recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[4]  Mohammed Bennamoun,et al.  A Curvelet-based approach for textured 3D face recognition , 2015, Pattern Recognit..

[5]  Horst Bunke,et al.  Classifying range images of human faces with Hausdorff distance , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[6]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Andrew Zisserman,et al.  Geometric invariance in computer vision , 1992 .

[8]  Syed Zulqarnain Gilani,et al.  Dense 3D Face Correspondence , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[10]  Liming Chen,et al.  Enhancing 3D Face Recognition By Mimics Segmentation , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[11]  Sridha Sridharan,et al.  Combined 2D/3D Face Recognition Using Log-Gabor Templates , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.

[12]  Chin Seng Chua,et al.  Point Signatures: A New Representation for 3D Object Recognition , 1997, International Journal of Computer Vision.

[13]  D. Struik.  Lectures on classical differential geometry , 1951 .

[14]  Maurício Pamplona Segundo,et al.  3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Ioannis A. Kakadiaris,et al.  UR3D-C: Linear dimensionality reduction for efficient 3D face recognition , 2011, 2011 International Joint Conference on Biometrics (IJCB).

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

[17]  Mohammed Bennamoun,et al.  An efficient 3D face recognition approach based on the fusion of novel local low-level features , 2013, Pattern Recognit..

[18]  Hongyu Li,et al.  3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation , 2014, PloS one.

[19]  Anuj Srivastava,et al.  Elastic Shape Models for Face Analysis Using Curvilinear Coordinates , 2009, Journal of Mathematical Imaging and Vision.

[20]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Feng Liu,et al.  Local Feature Tensor Based Deep Learning for 3D Face Recognition , 2019, 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019).

[22]  Tieniu Tan,et al.  Automatic 3D face recognition from depth and intensity Gabor features , 2009, Pattern Recognit..

[23]  Christoph von der Malsburg,et al.  Strategies and Benefits of Fusion of 2D and 3D Face Recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[24]  Faouzi Ghorbel.  Towards a unitary formulation for invariant image description: application to image coding , 1998, Ann. des Télécommunications.

[25]  Mohammed Bennamoun,et al.  A Two-Phase Weighted Collaborative Representation for 3D partial face recognition with single sample , 2016, Pattern Recognit..

[26]  Chin-Seng Chua,et al.  Facial feature detection and face recognition from 2D and 3D images , 2002, Pattern Recognit. Lett..

[27]  Liming Chen,et al.  A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[28]  Liming Chen,et al.  Location-sensitive sparse representation of deep normal patterns for expression-robust 3D face recognition , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[29]  Faisal R. Al-Osaimi,et al.  An Expression Deformation Approach to Non-rigid 3D Face Recognition , 2009, International Journal of Computer Vision.

[30]  Ajmal S. Mian,et al.  Deep, dense and accurate 3D face correspondence for generating population specific deformable models , 2017, Pattern Recognit..

[31]  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.

[32]  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.

[33]  Amal Rihani,et al.  An SE(3) invariant description for 3D face recognition , 2019, Image Vis. Comput..

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

[35]  Syed Zulqarnain Gilani,et al.  Learning from Millions of 3D Scans for Large-Scale 3D Face Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[36]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[37]  Dimitrios Hatzinakos,et al.  Iterative Closest Normal Point for 3D Face Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[39]  Liming Chen,et al.  A novel geometric facial representation based on multi-scale extended local binary patterns , 2011, Face and Gesture 2011.

[40]  Liming Chen,et al.  Towards 3D Face Recognition in the Real: A Registration-Free Approach Using Fine-Grained Matching of 3D Keypoint Descriptors , 2014, International Journal of Computer Vision.

[41]  M. Spivak.  A comprehensive introduction to differential geometry , 1979 .

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

[43]  Koji Hotta,et al.  View independent face recognition based on kernel principal component analysis of local parts , 2005, IEEE International Conference on Image Processing 2005.

[44]  J KriegmanDavid,et al.  Eigenfaces vs. Fisherfaces , 1997 .

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

[46]  Mehryar Emambakhsh,et al.  Nasal Patches and Curves for Expression-Robust 3D Face Recognition , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[47]  Paul Suetens,et al.  meshSIFT: Local surface features for 3D face recognition under expression variations and partial data , 2013, Comput. Vis. Image Underst..

[48]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

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

[50]  Ying Cai,et al.  A fast and robust 3D face recognition approach based on deeply learned face representation , 2019, Neurocomputing.

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

[52]  Hassen Drira,et al.  3D Face Recognition under Expressions, Occlusions, and Pose Variations , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[53]  Jongmoo Choi,et al.  Deep 3D face identification , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[54]  Anil K. Jain,et al.  Deformation Modeling for Robust 3D Face Matching , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[55]  Jun Wang,et al.  A 3D facial expression database for facial behavior research , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[56]  Faouzi Ghorbel,et al.  A robust invariant bipolar representation for R3 surfaces: applied to the face description , 2013, Ann. des Télécommunications.

[57]  Gaile G. Gordon,et al.  Face recognition based on depth maps and surface curvature , 1991, Optics & Photonics.

[58]  Faouzi Ghorbel,et al.  A parametric representation and feature matching technique for 3D rigid registration: application to 3D face description , 2014, Digital Image Processing.

[59]  Huibin Li,et al.  Expression-robust 3D face recognition via weighted sparse representation of multi-scale and multi-component local normal patterns , 2014, Neurocomputing.

[60]  Tieniu Tan,et al.  Robust 3D Face Recognition Using Learned Visual Codebook , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[61]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.