3D Face Recognition Using Spherical Vector Norms Map

In this paper, we introduce a novel, automatic method for 3D face recognition. A new feature called a spherical vector norms map of a 3D face is created using the normal vector of each point. This feature contains more detailed information than the original depth image in regions such as the eyes and nose. For certain flat areas of 3D face, such as the forehead and cheeks, this map could increase the distinguishability of different points. In addition, this feature is robust to facial expression due to an adjustment that is made in the mouth region. Then, the facial representations, which are based on Histograms of Oriented Gradients, are extracted from the spherical vector norms map and the original depth image. A new partitioning strategy is proposed to produce the histogram of eight patches of a given image, in which all of the pixels are binned based on the magnitude and direction of their gradients. In this study, SVNs map and depth image are represented compactly with two histograms of oriented gradients; this approach is completed by Linear Discriminant Analysis and a Nearest Neighbor classifier.

[1]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

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

[3]  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.

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

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

[6]  Liming Chen,et al.  Expression robust 3D face recognition via mesh-based histograms of multiple order surface differential quantities , 2011, 2011 18th IEEE International Conference on Image Processing.

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

[8]  Qiuqi Ruan,et al.  Three-dimensional face recognition under expression variation , 2014, EURASIP J. Image Video Process..

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

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

[11]  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.

[12]  Thomas Vetter,et al.  Expression invariant 3D face recognition with a Morphable Model , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[13]  Sridha Sridharan,et al.  Robust 3D Face Recognition from Expression Categorisation , 2007, ICB.

[14]  Luuk J. Spreeuwers,et al.  Fast and Accurate 3D Face Recognition , 2011, International Journal of Computer Vision.

[15]  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.

[16]  Gérard G. Medioni,et al.  Performance of Geometrix ActiveID^TM 3D Face Recognition Engine on the FRGC Data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

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

[18]  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).

[19]  Yunhong Wang,et al.  Robust 3D face recognition based on resolution invariant features , 2011, Pattern Recognit. Lett..

[20]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[22]  S B Puri,et al.  3D FACE RECOGNITION UNDER EXPRESSIONS, OCCLUSIONS AND POSE VARIATION , 2018 .

[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]  Ioannis A. Kakadiaris,et al.  Which parts of the face give out your identity? , 2011, CVPR 2011.

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