CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Face Detection and Recognition based on Fusion of Omnidirectional and PTZ Vision Sensors and Heteregenous Database

by Redouane Khemmar, Jean Yves Ertaud, Xavier Savatier
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 21
Year of Publication: 2013
Authors: Redouane Khemmar, Jean Yves Ertaud, Xavier Savatier
10.5120/10207-5000

Redouane Khemmar, Jean Yves Ertaud, Xavier Savatier . Face Detection and Recognition based on Fusion of Omnidirectional and PTZ Vision Sensors and Heteregenous Database. International Journal of Computer Applications. 61, 21 ( January 2013), 35-44. DOI=10.5120/10207-5000

@article{ 10.5120/10207-5000,
author = { Redouane Khemmar, Jean Yves Ertaud, Xavier Savatier },
title = { Face Detection and Recognition based on Fusion of Omnidirectional and PTZ Vision Sensors and Heteregenous Database },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 21 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 35-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number21/10207-5000/ },
doi = { 10.5120/10207-5000 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:10:15.167143+05:30
%A Redouane Khemmar
%A Jean Yves Ertaud
%A Xavier Savatier
%T Face Detection and Recognition based on Fusion of Omnidirectional and PTZ Vision Sensors and Heteregenous Database
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 21
%P 35-44
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Large field of view with high resolution has always been soughtafter for Mobile Robotic Authentication. So the Vision System proposed here is composed of a catadioptric sensor for full range monitoring and a Pan Tilt Zoom (PTZ) camera together forming an innovative sensor, able to detect and track any moving objects at a higher zoom level. In our application, the catadioptric sensor is calibrated and used to detect and track Regions Of Iinterest (ROIs) within its 360 degree Field Of View (FOV), especially face regions. Using a joint calibration strategy, the PTZ camera parameters are automatically adjusted by the system in order to detect and track the face ROI within a higher resolution and project the same in facespace for recognition via Eigenface algorithm. Face recognition is one important task in Nomad Biometric Authentication (NOBA1) project. However, as many other face databases, it will easily produce the Small Sample Size (SSS) problem in some applications with NOBA data. Thus this journal uses the compressed sensing (CS) algorithm to solve the SSS problem in NOBA face database. Some experiments can prove the feasibility and validity of this solution. The whole development has been partially validated by application to the Face recognition using our own database NOBA.

References
  1. S. Baker and S. K. A theory of single-viewpoint catadioptric image formation. International Journal of Computer Vision. , 103(3):175-196, 2006.
  2. J. Barreto. A unifying geometric representation for central projection systems. Computer Vision and Image Understanding. , 103(3):208-217, September 2006.
  3. R. Boutteau. Reconstruction tridimensionnelle de l´environnement d´un robot mobile a` partir d´informations de vision omnidirectionnelle pour la preparation. PhD Thesis. , University de rouen, 2009.
  4. R. S. D. Scaramuzza, and A. Martinelli. A flexible technique for accurate omnidirectional camera calibration and structure from motion. Proceedings of the International Conference on Computer Vision. , 45-52, January 2006.
  5. Y. Freund and E. Schapire. A decision-theoritic generalization of on-line learning and an application to boosting. Journal of Computer and System Science. , 1997.
  6. Geyer and K. Daniilidis. A unifying theory for central panoramic systems and practical implications. Proceedings of the European Conference on Computer Vision. , 445-461, 2000.
  7. A. Iraqui. H, Y. Dupuis, R. Boutteau, J. -Y. Ertaud, and X. Savatier. Fusion of omnidirectional and ptz cameras for face detection and tracking. In International Conference on Emerging Security Technologies (EST). , 2010.
  8. Levenberg. A method for the solution of certain probless in least squares. Quarterly of Applied Mathematics,. 2:164-168, 1994.
  9. C. Mei and P. Rives. Single view point omnidirectional camera calibration from planar grids. In Proceedings of the International Conference on Robotics and Automation (ICRA). , 3945-3950, 2007.
  10. S. R. Ramalingram, and P. Lodha Sturn. Towards complete generic camera calibration. In Proceedings of the International Conference on Computer Vision and Pattern Recognition. , 767- 769, June 2005.
  11. M. Turk and A. Pentland. Eigenfaces for recognition. In Vision and Modeling Group The Media Laboratory. , 1994.
  12. P. Viola and M. Jones. Robust real-time object detection. International Journal of Computer Vision. , 2001.
  13. E. Cand`es, J. Romberg, and T. Tao. Robust uncertainty principles : Exact signal reconstructionfrom highly incomplete frequency information. IEEE Transactions on Information Theory. , 52(2):489-509, 2006.
  14. E. Cand`es, J. Romberg,and T. Tao. Near optimal signal recovery from random projections : Universal encoding strategies. IEEE Transactions on Information Theory. , 52(12):5406-5425, 2006.
  15. D. L. Donoho. Compressed sensing. IEEE Transactions on Information Theory. , 52(4):1289-1306, 2006.
  16. L. Qiao, S. Chen, X. Tan. Sparsity preserving projections with applications to face recognition. Pattern Recognition. , 43(1):331-341, 2010.
  17. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. , 31(2):210- 227, 2009.
  18. T. Ahonen, A. Hadid, and M. Pietikainen. Face Description with Local Binary Patterns: Application to Face Recognition. IEEE PAMI. , 28(12):2037-2041, 2006.
  19. K. Koh, S. J. Kim, and S. Boyd. An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression. The Journal of Machine Learning Research. , 8:1519-1555, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

face recognition compressed sensing nomad biometric authentication eigenface recognition omnidirectional sensor

Powered by PhDFocusTM