CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Implementation of Feature Selection Using Correlation Matrix in Python

Ahmad Farhan AlShammari
Random Articles
Reseach Article

Classification of Imagery Data and Face Recognition Techniques

by Neeraj Pratap, Shwetank, Vikesh Kumar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 85 - Number 10
Year of Publication: 2014
Authors: Neeraj Pratap, Shwetank, Vikesh Kumar
10.5120/14876-3272

Neeraj Pratap, Shwetank, Vikesh Kumar . Classification of Imagery Data and Face Recognition Techniques. International Journal of Computer Applications. 85, 10 ( January 2014), 21-26. DOI=10.5120/14876-3272

@article{ 10.5120/14876-3272,
author = { Neeraj Pratap, Shwetank, Vikesh Kumar },
title = { Classification of Imagery Data and Face Recognition Techniques },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 85 },
number = { 10 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 21-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume85/number10/14876-3272/ },
doi = { 10.5120/14876-3272 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:02:05.298751+05:30
%A Neeraj Pratap
%A Shwetank
%A Vikesh Kumar
%T Classification of Imagery Data and Face Recognition Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 85
%N 10
%P 21-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A lot of research work has been done by the researchers in the field of face recognition. These days many innovative issues of research and application in the field of face recognition are still pending and required to be discuss and develop. Different studies on face recognition already have been done and implemented but suffering from a single view point, applications and methods, because of traditional imagery input data. This paper explores and classifies the different input imagery data: traditional images, videos (sequence of images with time interval) and 3D images, considered to develop the face recognition techniques: signal processing, machine learning and multidimensional face recognition. The key feature of this study is to introduce a new era of face recognition system and technology (input sources, effects, techniques, assessment, limitations etc. ) based on Multidimensional Imagery Data known as Multidimensional Face Recognition System (MFRS).

References
  1. M. Nixon, 1985, Eye spacing measurement for facial recognition, Proc. of SPIE on Applications of Digital Image Processing VIII, vol 575, pp. 279-285.
  2. N. Roeder and X. Li, 1995, Experiments in analyzing the accuracy of facial feature detection, Proc. of Canadian Conference on Vision Interface, pp. 8-16.
  3. I. J. Cox, J. Ghosn, and P. N. Yianilos, 1996. Feature based face recognition using mixture-distance, Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 209-216.
  4. A. P. Kumar, S. Das, and V. Kamakoti, 2001, Face recognition using weighted modular principle component analysis, Proc. of 11th International Conference Neural Information Processing, vol 3316, pp. 362-367.
  5. J. Yang and D. Zhang, 2004, Two-dimensional PCA: a new approach to appearance-based face representation and recognition, IEEE Trans. Pattern Analysis and Machine Intelligence, vol 28, pp. 131-137.
  6. J. Meng and W. Zhang, Journal of Pattern Recognition Letters, vol. 28, pp. 1203-1208, 2007. Volume measure in 2D PCA based face recognition.
  7. M. A. O. Vasilescu and D. Terzopoulos, 2003, Multilinear subspace analysis of image ensembles, Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 93-99.
  8. Q. Yang and X. Q. Ding, 2003, Symmetrical principal component analysis and its application in face recognition, Chinese Journal of Computers, vol 26, pp. 1146-1151.
  9. L. Torres, L. Lorente, and J. Vila, 2000, Automatic face recognition of video sequences using self eigen faces, Proc. of International Symposium on Image/Video Communications over Fixed and Mobile Networks, pp. 44-47.
  10. R. Chellappa, C. L. Wilson, and S. Sirohey, 1995, Human and machine recognition of faces: a survey, Proc. of the IEEE, vol 83, pp. 705-740,
  11. A. Howell and H. Buxton, 1996, Towards unconstrained face recognition from image sequences, Proc. of the Second IEEE International Conference on Automatic Face and Gesture Recognition, pp. 224-229.
  12. A. Schwaninger, and J. Yang, 2011, the application of 3D representations in face recognition, Journal of Vision Research, vol 51, pp. 969-977.
  13. C. Liu and H. Wechsler, 2002, Gabor feature based classi?cation using the enhanced ?sher linear discriminant model for face recognition, IEEE Trans. Image Processing, vol 11, no. 4, pp. 467-476.
  14. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, 1997, Eigen faces vs. ?sher faces: recognition using class speci?c linear projection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol 19, no. 7, pp. 711-720,
  15. K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, 2001, An introduction to kernel-based learning algorithms, IEEE Trans. Neural Networks, vol 12, pp. 181-201.
  16. C. Y. Chang, C. W. Chang, and C. Y. Hsieh, 2011, Applications of block linear discriminant analysis for face recognition, Journal of Information Hiding and Multimedia Signal Processing, vol 2, no. 3, pp. 259-269.
  17. H. Yin, 2002, Data visualization and manifold mapping using the ViSOM, Journal of Neural Networks, vol 15, no. 8, pp. 1005-1016.
  18. J. B. Tenenbaum, V. Silva, and J. C. Langford, 2000, global geometric frames work for nonlinear dimensionality reduction, Journal of Science, vol 290, no. 5500, pp. 2319-2323.
  19. S. T. Roweis and L. K. Saul, 2000, Nonlinear dimensionality deduction by locally linear embedding, Journal of Science, vol 290, no. 5500, pp. 2323-2326.
  20. Y. Xu, G. Feng, and Y. Zhao, 2009, One improvement to two-dimensional locality preserving projection method for use with face recognition, Journal of Neuro computing, vol 73, pp. 245-249.
  21. J. Lu, K. N. Plataniotis and A. N. Venetsanopoulos, 2003, Face recognition using kernel direct discriminant analysis algorithms, IEEE Transactions on Neural Networks, vol 14, no. 1, pp. 117-226.
  22. G. Baudat and F. Anouar, 2000, Generalized discriminant analysis using a kernel approach, Journal of Neural Computation, vol 12, no. 10, pp. 2385-2404.
  23. M. H. Yang, 2002, Kernel eigen faces vs. kernel ?sher faces: face recognition using kernel methods, Proc. of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 215-220,
  24. Neeraj Pratap, Shwetank Arya, June 2012, A Review of Devnagari Character Recognition from Past to Future, International Journal of Computer Science and Telecommunications, vol 3, issue 6, pp. 77-82.
  25. Dong-Lin Li, Mukesh Prasad, Sheng-Chih Hsu, Chao-Ting Hong and Chin-Teng Lin, 2012 , Face recognition using nonparametric-weighted Fisherfaces, EURASIP Journal on Advances in Signal Processing, vol 1 , no. 1, pp. 1-11.
  26. Wei Di, Lei Zhang, David Zhang, and Quan Pan, November 2010, Studies on Hyperspectral Face Recognition in Visible Spectrum with Feature Band Selection, IEEE Transactions on Systems, Man, and Cybernetics — Part a: Systems and Humans, vol 40, no. 6, pp. 1354-1361.
  27. Andrew Wimberlya, Stefan A. Robila, Tansy Peplauc, 2010, Spectral Face Recognition Using Orthogonal Subspace Base", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76952E.
  28. Petra Koruga, Jurica Ševa, Miroslav Ba?a, 2011, A Review of Face Recognition Algorithms and their Application in Age Estimation, Proceedings of the 22nd Central European Conference on Information and Intelligent Systems, pp. 127-132.
  29. Amit Mukherjee, Miguel Velez-Reyes, and Badrinath Roysam, 2009, Interest Points for Hyperspectral Image Data, IEEE Transactions on Geoscience and Remote Sensing, vol 47, no. 3, pp. 748-760.
  30. Zhihong Pan, Glenn Healey, Manish Prasad, and Bruce Tromberg, 2003, Face Recognition in Hyperspectral Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 25, no. 12, pp. 1552-1560.
  31. R. Brunelli and T. Poggio, 1993, Face recognition: features versus templates, IEEE Trans. Pattern Analysis and Machine Intelligence, vol 15, pp. 1042-1052.
  32. Shwetank, Kamal Jain and Karamjit Bhatia, Development of Digital Spectral Library and Supervised Classification of Rice Crop Varieties Using Hyperspectral Image Processing, Asian Journal of Geoinformatics, vol 11, no. 3.
  33. A. Pentland, B. Moghaddam, and T. Starner, 1994, View based and modular eigen spaces for face recognition, Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 84-90.
  34. A Mohammad-Djafari, A Mohammadpour, 2005, Hyperspectral image processing using a Bayesian classification approach- Proc. PSIP.
  35. T. J. Roper and M. Andrews, Development of a Hyperspectral skin database.
Index Terms

Computer Science
Information Sciences

Keywords

Face Recognition Sensory Inputs Manifold learning Hyperspectral Image

Powered by PhDFocusTM