Geometric Histograms of 3D Keypoints for Face Identification with Missing Parts

In this work, an original solution to 3D face identification is proposed, which supports recognition also in the case of probes with missing parts. Distinguishing traits of the face are captured by first extracting 3D keypoints of a face scan, then measuring how the face surface changes in the keypoints neighborhood using a local descriptor. To this end, an adaptation of the meshDOG algorithm to the case of 3D faces is proposed, together with a multi-ring geometric histogram descriptor. Face similarity is then evaluated by comparing local keypoint descriptors across inlier pairs of matching keypoints between probe and gallery scans. Experiments have been performed to assess the keypoints distribution and repeatability. Recognition accuracy of the proposed approach has been evaluated on the Bosphorus database, showing competitive results with respect to existing 3D face biometrics solutions.

[1]  Radu Horaud,et al.  Surface feature detection and description with applications to mesh matching , 2009, CVPR.

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

[3]  B. S. Manjunath,et al.  The multiRANSAC algorithm and its application to detect planar homographies , 2005, IEEE International Conference on Image Processing 2005.

[4]  Federico Tombari,et al.  Performance Evaluation of 3D Keypoint Detectors , 2012, International Journal of Computer Vision.

[5]  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..

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

[7]  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.

[8]  Naoufel Werghi,et al.  An ordered topological representation of 3D triangular mesh facial surface: concept and applications , 2012, EURASIP J. Adv. Signal Process..

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

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

[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]  Radu Horaud,et al.  SHREC '11: Robust Feature Detection and Description Benchmark , 2011, 3DOR@Eurographics.

[13]  Robert B. Fisher,et al.  Finding Surface Correspondance for Object Recognition and Registration Using Pairwise Geometric Histograms , 1998, ECCV.

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

[15]  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).

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

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

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