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3D Face Recognition using Gaussian Hermite Moments

Published on September 2012 by Naouar Belghini, Arsalane Zarghili, Jamal Kharroubi
Software Engineering, Databases and Expert Systems
Foundation of Computer Science USA
SEDEX - Number 1
September 2012
Authors: Naouar Belghini, Arsalane Zarghili, Jamal Kharroubi
f1ec536d-d62b-441e-931a-87251c234b83

Naouar Belghini, Arsalane Zarghili, Jamal Kharroubi . 3D Face Recognition using Gaussian Hermite Moments. Software Engineering, Databases and Expert Systems. SEDEX, 1 (September 2012), 1-4.

@article{
author = { Naouar Belghini, Arsalane Zarghili, Jamal Kharroubi },
title = { 3D Face Recognition using Gaussian Hermite Moments },
journal = { Software Engineering, Databases and Expert Systems },
issue_date = { September 2012 },
volume = { SEDEX },
number = { 1 },
month = { September },
year = { 2012 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /specialissues/sedex/number1/8350-1001/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Software Engineering, Databases and Expert Systems
%A Naouar Belghini
%A Arsalane Zarghili
%A Jamal Kharroubi
%T 3D Face Recognition using Gaussian Hermite Moments
%J Software Engineering, Databases and Expert Systems
%@ 0975-8887
%V SEDEX
%N 1
%P 1-4
%D 2012
%I International Journal of Computer Applications
Abstract

Face recognition is an interesting issue in pattern recognition. In this paper, we propose a method for face recognition using 3D depth information. The goal is to get minimum features and produce a good recognition rates. We extract 3D clouds points from 3d vrml face Database, then the nose tip for each sample is detected and considered as new origin of the coordinate system, Gaussian Hermite Moments are applied to characterize each individual and Back propagation neural network is applied for the recognition task. Experimental results shows that Gaussian Hermite moments with global depth information perform significantly better than another method based on local depth information, in this study we consider the case of using ratios of distances and angles between manually selected facial fiducial points.

References
  1. Phillips, P. J. , Scruggs, W. T. , O'Toole, A. J. , Flynn, P. J. , Bowyer, K. W. , Schott, C. L. , Sharpe, M. 2007. FRVT 2006 and ICE 2006 Large-Scale Results, NISTIR 7408. National Institute of Standards and Technology.
  2. Jafri, R and Arabnia, R. "A Survey of Face Recognition Techniques", Journal of Information Processing Systems, Vol. 5, No. 2, 2009.
  3. Andrea, F. , Michele, N. , Riccio, D. , Sabatino, G. , "2D and 3D face recognition: A survey", Pattern Recognition Letters, Vol. 28, No. 14, 2007, 1885-1906.
  4. Pears,N. , Heseltine,T. , Romero,M. , "From 3D Point Clouds to Pose-Normalised Depth Maps", Int J Comput Vis, 2010, 152-176.
  5. Wang,C. , Niu,X. , Lu,W. , Gong,J. , "A Hybrid Method to Build a Canonical Face Depth Map", International Journal of Digital Content Technology and its Applications, Vol. 5, No. 5, May 2011.
  6. Alessandro Colombo, Claudio Cusano, Raimondo Schettini, "3D face detection using curvature analysis", Pattern Recognition Vol. 39, No. 3, 2006, 444-455.
  7. Khalid,F. , Lili, N. A. , "3D Face Recognition Using Multiple Features for Local Depth Information", IJCSNS International Journal of Computer Science and Network Security, Vol. 9 No. 1, 2009
  8. Gupta, S. , Aggarwal, J. K. , Markey, M. K. , Bovik, A. C. 2007. 3D Face Recognition Founded on the Structural Diversity of Human Faces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR '07.
  9. Bo Yang, Mo Dai, "Image analysis by Gaussian–Hermite moments", Signal Processing, 2011, 2290–2303.
  10. Jing YANG, Guohua PENG, "Subpixel Edge Location Based on Gaussian-Hermite Moments", Journal of Information & Computational Science, Vol. 8,No. 14, 2011, 3131–3140.
  11. Youfu Wu, Jun Shen, "Properties of Orthogonal Gaussian-Hermite Moments and Their Applications", EURASIP Journal on Applied Signal Processing, 2005, 588–599.
  12. Xu, C. , Wang, Y. , Tan, T. , Quan, L. 2004. Automatic 3D face recognition combining global geometric features with local shape variation information. In the Proceedings of the Sixth Int. Conf. on Automated Face and Gesture Recognition, 308–313.
  13. Flusser, J. 2006. Moment Invariants in Image Analysis, Ttransactions on engineering, computing and technology.
  14. Aarif, T. , Shaaban,Z. , Krekor,L. , baba, A. , "object classification via geometrical, zernike and legendre moments", Journal of Theoretical and Applied Information Technology, Vol. 7, No. 1, 2009, 31 -37.
  15. Shen, J. 1997. Orthogonal Gaussian-Hermite Moments for Image Characterization. In Proceedings of Intelligent Robots and Computer Vision XVI: Algorithms Techniques, Active Vision, and Materials Handling, 224-233.
  16. Shen, J. ; Shen, W. & Shen, D. , "On Geometric and orthogonal moments". International Journal of Pattern Recognition and Artificial Intelligence, Vol. 14, No. 7, 2000, 875-894.
  17. http://frav. escet. urjc. es/databases/FRAV3D/
  18. Farkas, LG and Munro. 1987 Anthropometric Facial Proportions in Medicine. Charles C Thomas: Springfield.
  19. Y. Wang, C. Chua, and Y. Ho. Facial feature detection and face recognition from 2d and 3d images. Pattern Recognition Letters, Vol. 23, No. 10, 2002, 1191–1202.
  20. Gupta, S. , Castleman, K. R. , Markey, M. K. & Bovik. 2010. Texas 3d face recognition database. In Image analysis and interpretation, IEEE Southwest Symposium on, Austin, TX.
Index Terms

Computer Science
Information Sciences

Keywords

Gaussian Hermite Moments 3d Face Recognition Back Propagation Neural Network