Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm

Liela Khobanizad; Mahmood Khobanizad; Behrouz Vaseghi; Hamid Chegini

doi:doi:10.11648/j.wcmc.20160402.13

| Peer-Reviewed

Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm

Liela Khobanizad, Mahmood Khobanizad, Behrouz Vaseghi, Hamid Chegini

Published in International Journal of Wireless Communications and Mobile Computing (Volume 4, Issue 2)

Received: 16 March 2016 Accepted: 31 March 2016 Published: 15 April 2016

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

One way of consideration for identifying the human IS recognition of face by portable tools like mobile and tablet. One challenge is low power in portable android tools for face recognition (identification), so GPU must be used in software connection central Graphic processor which has a good function, compared to present processors in today portable android tools. Binary pattern (local) is one of the methods that are used for characteristic production and the image stratification. In this study, it is suggested to use connection and local binary pattern histogram algorithm to use optimum software open CV and using hardware platform android to identify the face.

Published in	International Journal of Wireless Communications and Mobile Computing (Volume 4, Issue 2)
DOI	10.11648/j.wcmc.20160402.13
Page(s)	25-31
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Face Recognition, Opencv, Android, LBT Algorithm

References

[1]	W. Zhao, R. Chellappa, P. J. Phillips and A. Rosenfeld, “Face recognition: A literature survey Acm Computing Surveys (CSUR)”, Vol. 35, no. 4, pp. 399-458, 2003.
[2]	P. Belhumeur, J. P. Lanfang, and D. Kriegman, “Eigenfacesvs. fisherfaces: Recognition using class specific linear projection ”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 19, no. 7, pp. 711-720, March. 1997.
[3]	M. Sharkas, and M. A. Elenien, “Eigenfaces vs. Fisherfaces vs. ICA for facerecognition; a comparative study,” Signal Processing, 2008. ICSP 2008.9thInternational Conference on. IEEE, pp. 914–919, Oct. 2008.
[4]	A. Ozdil, and M. M. Ozbilen, “A Survey on Comparison of Face Recognition Algorithms,” Application of Information and Communication Technologies (AICT), 2014 IEEE 8th International Conference on, pp. 1–3, Oct. 2014.
[5]	X. Lu, “Image Analysis for Face Recognition,” http://www.facerec.org/interest.ingpapers/General/ImAna4FacRcg_lu.pdf
[6]	Gonzalez, Rafael C., and Richard E. Woods, “Digital image processing,” (2002).
[7]	W. W. Bledsoe. The model method in facial recognition. Technical report pri 15, Panoramic Research, Inc., Palo Alto, California, 1964.
[8]	W. W. Bledsoe. Man-machine facial recognition: Report on a largescale experiment. Technical report pri 22, Panoramic Research, Inc., Palo Alto, California, 1966.
[9]	W. W. Bledsoe. Some results on multicategorypatten recognition. Journal of the Association for Computing Machinery, 13(2): 304–316, 1966.
[10]	W. W. Bledsoe. Semiautomatic facial recognition. Technical rep ort sri project 6693, Stanford Research Institute, Menlo Park, California, 1968.
[11]	W. W. Bledsoe and H. Chan. A man-machine facial recognition systemsome preliminary results. Technical report pri 19a, Panoramic Research, Inc., Palo Alto, California, 1965.
[12]	M. Fischler and R. Elschlager. The representation and matching of pictorial structures. IEEE Transactions on Computers, C-22(1):67–92, 1973.
[13]	T. J. Stonham. Practical face recognition and verification with wisard. In H. D. Ellis, editor, Aspects of face processing. Kluwer Academic Publishers, 1986.
[14]	M. Turk and A. Pentland.Eigenfaces for recognition. Journal of Cognitive Neurosicence, 3(1): 71–86, 1991.
[15]	M. McWhertor. “sony spills more ps3 motion controller details to devs”. Kotaku. Gawker Media., June 19 2009. http://kotaku.com/5297265/sony-spills-more-ps3-motion-controllerdetails-to-devs
[16]	Z. Pan, A. G. Rust, and H. Bolouri, “Image Redundancy Reduction for Neural Network Classification Using Discrete Cosine Transforms,” Proc. Int. Joint Conf. on Neural Networks, Vol. 3, (Como, Italy), pp. 149-154, 2000.
[17]	J. K. Sing, D. K. Basu, M. Nasipuri and M. Kundu, “Face Recognition Using Point Symmetry Distance Based RBF Network,” Jour. of Applied Soft Computing, 2005.
[18]	M. J. Er, S. Wu, J. Lu and H. L. Toh, “Face Recognition with Radial Basis Function (RBF) Neural Networks,” IEEE Trans. on Neural Networks, Vol. 3, No. 3, pp. 697-710, 2002.
[19]	Kirby, M., and Sirovich, L, “Application of the Karhunen-Loeve procedure forthe characterization of human faces,” IEEE PAMI, Vol. 12, pp. 103-108, (1990).
[20]	Sirovich, L., and Kirby, M., “Low-dimensional procedure for thecharacterization of human faces,” J. pp. 519-524, (1987). Opt. Soc. Am. A, 4, 3.
[21]	H. Anton, Elementary Linear Algebra 5e, John Wiley & Son Inc, 1987.
[22]	M. Turk and A. Pentland, “Face Recognition Using Eigenfaces,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1991, pp. 586-591.
[23]	Raudys and A. K. Jain. Small sample size effects in statistical pattern recognition: Recommendations for practitioneers. - IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 3 (1991), 252-264.
[24]	T. Ojala, M. Pietikainen and T. T. Maenpaa. Multiresolution gray-scale and rotation invariant textureclassification with local binary pattern. IEEE Transactionson Pattern Analysis and Machine Intelligence. 24(7): 971-987, 2002.
[25]	Gary, Bradski., and Adrian. Kaehler. "Learning Open CV." (2008).
[26]	J. F. Dimarzio "Android a programmers guide." (2008).

Cite This Article

Plain Text BibTeX RIS

APA Style

Liela Khobanizad, Mahmood Khobanizad, Behrouz Vaseghi, Hamid Chegini. (2016). Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm. International Journal of Wireless Communications and Mobile Computing, 4(2), 25-31. https://doi.org/10.11648/j.wcmc.20160402.13

Copy | Download

ACS Style

Liela Khobanizad; Mahmood Khobanizad; Behrouz Vaseghi; Hamid Chegini. Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm. Int. J. Wirel. Commun. Mobile Comput. 2016, 4(2), 25-31. doi: 10.11648/j.wcmc.20160402.13

Copy | Download

AMA Style

Liela Khobanizad, Mahmood Khobanizad, Behrouz Vaseghi, Hamid Chegini. Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm. Int J Wirel Commun Mobile Comput. 2016;4(2):25-31. doi: 10.11648/j.wcmc.20160402.13

Copy | Download

@article{10.11648/j.wcmc.20160402.13,
  author = {Liela Khobanizad and Mahmood Khobanizad and Behrouz Vaseghi and Hamid Chegini},
  title = {Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {4},
  number = {2},
  pages = {25-31},
  doi = {10.11648/j.wcmc.20160402.13},
  url = {https://doi.org/10.11648/j.wcmc.20160402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20160402.13},
  abstract = {One way of consideration for identifying the human IS recognition of face by portable tools like mobile and tablet. One challenge is low power in portable android tools for face recognition (identification), so GPU must be used in software connection central Graphic processor which has a good function, compared to present processors in today portable android tools. Binary pattern (local) is one of the methods that are used for characteristic production and the image stratification. In this study, it is suggested to use connection and local binary pattern histogram algorithm to use optimum software open CV and using hardware platform android to identify the face.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm
AU  - Liela Khobanizad
AU  - Mahmood Khobanizad
AU  - Behrouz Vaseghi
AU  - Hamid Chegini
Y1  - 2016/04/15
PY  - 2016
N1  - https://doi.org/10.11648/j.wcmc.20160402.13
DO  - 10.11648/j.wcmc.20160402.13
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 25
EP  - 31
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20160402.13
AB  - One way of consideration for identifying the human IS recognition of face by portable tools like mobile and tablet. One challenge is low power in portable android tools for face recognition (identification), so GPU must be used in software connection central Graphic processor which has a good function, compared to present processors in today portable android tools. Binary pattern (local) is one of the methods that are used for characteristic production and the image stratification. In this study, it is suggested to use connection and local binary pattern histogram algorithm to use optimum software open CV and using hardware platform android to identify the face.
VL  - 4
IS  - 2
ER  -

Copy | Download

Author Information

Liela Khobanizad

Telecommunication of Non-profit Institution of Higher Education, ABA, Abyek, Qazvin, Iran
Mahmood Khobanizad

Electrical Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran
Behrouz Vaseghi

Electrical Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran
Hamid Chegini

Non-profit Institution of Higher Education, ABA, Abyek, Qazvin, Iran

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Liela Khobanizad, Mahmood Khobanizad, Behrouz Vaseghi, Hamid Chegini. (2016). Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm. International Journal of Wireless Communications and Mobile Computing, 4(2), 25-31. https://doi.org/10.11648/j.wcmc.20160402.13

Copy | Download

ACS Style

Liela Khobanizad; Mahmood Khobanizad; Behrouz Vaseghi; Hamid Chegini. Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm. Int. J. Wirel. Commun. Mobile Comput. 2016, 4(2), 25-31. doi: 10.11648/j.wcmc.20160402.13

Copy | Download

AMA Style

Liela Khobanizad, Mahmood Khobanizad, Behrouz Vaseghi, Hamid Chegini. Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm. Int J Wirel Commun Mobile Comput. 2016;4(2):25-31. doi: 10.11648/j.wcmc.20160402.13

Copy | Download

@article{10.11648/j.wcmc.20160402.13,
  author = {Liela Khobanizad and Mahmood Khobanizad and Behrouz Vaseghi and Hamid Chegini},
  title = {Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {4},
  number = {2},
  pages = {25-31},
  doi = {10.11648/j.wcmc.20160402.13},
  url = {https://doi.org/10.11648/j.wcmc.20160402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20160402.13},
  abstract = {One way of consideration for identifying the human IS recognition of face by portable tools like mobile and tablet. One challenge is low power in portable android tools for face recognition (identification), so GPU must be used in software connection central Graphic processor which has a good function, compared to present processors in today portable android tools. Binary pattern (local) is one of the methods that are used for characteristic production and the image stratification. In this study, it is suggested to use connection and local binary pattern histogram algorithm to use optimum software open CV and using hardware platform android to identify the face.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Implement of Face Recognition in Android Platform by Using Opencv and LBT Algorithm
AU  - Liela Khobanizad
AU  - Mahmood Khobanizad
AU  - Behrouz Vaseghi
AU  - Hamid Chegini
Y1  - 2016/04/15
PY  - 2016
N1  - https://doi.org/10.11648/j.wcmc.20160402.13
DO  - 10.11648/j.wcmc.20160402.13
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 25
EP  - 31
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20160402.13
AB  - One way of consideration for identifying the human IS recognition of face by portable tools like mobile and tablet. One challenge is low power in portable android tools for face recognition (identification), so GPU must be used in software connection central Graphic processor which has a good function, compared to present processors in today portable android tools. Binary pattern (local) is one of the methods that are used for characteristic production and the image stratification. In this study, it is suggested to use connection and local binary pattern histogram algorithm to use optimum software open CV and using hardware platform android to identify the face.
VL  - 4
IS  - 2
ER  -

Copy | Download