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Reseach Article

Next Generation Face Recognition System under Various Illuminations

Published on February 2013 by S. Zeenathunisa
International Conference on Communication, Computing and Information Technology
Foundation of Computer Science USA
ICCCMIT - Number 1
February 2013
Authors: S. Zeenathunisa
b0dc6b80-09aa-42e8-aa69-33726fce0da8

S. Zeenathunisa . Next Generation Face Recognition System under Various Illuminations. International Conference on Communication, Computing and Information Technology. ICCCMIT, 1 (February 2013), 35-42.

@article{
author = { S. Zeenathunisa },
title = { Next Generation Face Recognition System under Various Illuminations },
journal = { International Conference on Communication, Computing and Information Technology },
issue_date = { February 2013 },
volume = { ICCCMIT },
number = { 1 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 35-42 },
numpages = 8,
url = { /specialissues/icccmit/number1/10328-1011/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 International Conference on Communication, Computing and Information Technology
%A S. Zeenathunisa
%T Next Generation Face Recognition System under Various Illuminations
%J International Conference on Communication, Computing and Information Technology
%@ 0975-8887
%V ICCCMIT
%N 1
%P 35-42
%D 2013
%I International Journal of Computer Applications
Abstract

Identification via face recognition offers enormous advantages to both civilian and criminal detection programs. A next generation system that has the potential to recognize such wanted criminals from a large dataset considered to be used for security of the public safety. It also has the prospective to provide high data rates at low power over various illumination conditions in the, to be identified images. This face recognition technique for next generation acts as a baseline for the development of future solutions for response and recovery. This research is a new state-of-art integrating various techniques to perceive the faces using Illumination Normalization, Feature Extraction and Classification. The Illumination Normalization is useful for removing the dimness and shadow from the facial image which obtained from the large dataset reduces the effect of illumination variations, but, still retains the necessary information of the face. The robust local feature extractor which is the gray-scale invariant texture called Local Binary Pattern (LBP) is helpful for feature extraction. The K-Nearest Neighbor classifier is utilized for the purpose of classification and matching the face images from the dataset. Thus, the next generation system tends to identify and recognize the input face image after preprocessing the image and feature extraction. Various images for the system from Yale-B database are used for testing to achieve the next generation face recognition system which helps in improving the performance in identifying and recognizes the faces in various illuminations.

References
  1. Cha Zhang and Zhengyou Zhang, "A Survey of Recent Advances in Face Detection", Technical Report, MSR-TR-2010-66, June 2010
  2. A. Gupta, S. Gupta, "Machine Recognition of Human Face", www. citeseerx. ist. psu. edu/viewdoc/download, dt. May 2011.
  3. Xiaoyang Tan and Bill Triggs, "Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions", IEEE Trans, on Image Processing, Vol. 19, No. 6, June 2010
  4. Brendan Moore, Marshall Tappen and Hassan Foroosh, "Learning Face Appearance under Different Lighting Conditions", In the 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, 2008.
  5. D. Alexiadis, V. Syrris, A. Papastergiou, A. Hatzigaidas and L. Mariuta, "A New Face Database and Evaluation of Face Recognition Techniques",14th WSEAS Intern. Conf. on Systems, Vol–II, pp. 590-595
  6. Daniel Maturana, Domingo Mery and Alvaro Soto, "Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naïve Bayes Nearest Neighbor Classification", In the International Conference of the Chilean Computer Science Society, pp. 125 – 132, 2009
  7. Jukka Holappa, Timo Ahonen and Matti Pietikäinen, "An Optimized Illumination Normalization Method for Face Recognition", In Proc. of IEEE, 2nd International Conference on Biometrics: Theory, Applications and Systems (BTAS 08), Washington DC, 6 p.
  8. Timo Ahonen, Abdenour Hadid and Matti Pietikäinen, "Face Description with Local Binary Patterns: Application to Face Recognition", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol 28, No. 12, pp. 2037 – 2041, Dec 2006.
  9. Padraig Cunnigham and Sarah Jane Dilany, "k – Nearest Neighbour Classifier", Technical Report UCD-CSI-2007-4, March 2007
  10. Yangang Wang, Xi Chen, Hua Han and Silong Peng, "Video Luminance Transient Improvement Using Difference of Gaussian", In Proc. of 15th Asia-Pacific Conference on Communications (APCC 2009)-058, pp. 249 – 253, 2009.
  11. G. Aggarwal and R. Chellappa, "Face Recognition in the presence of multiple illumination sources", In Proc. of the IEEE International Conference on Computer Vision (ICCV '05), pp. 1169 – 1176, 2005
  12. M. A. Rabbani and C. Chellappan, "An Effective Approach to Frontal Face Recognition Using Distance Measures", In the Proceedings of Asian Journal of Information Technology, 4(12), pp. 1110 – 1115, 2005
  13. Claudio A. Perez, Luis E. Castillo, and Leonardo A. Cament, "Illumination Compensation Method for Local Matching Gabor Face Classifier", In the Proceedings of International Symposium on Optomechatronic Technologies (ISOT 2010), October 2010.
  14. Timo Ojala, Matti Pietikäinen, and Topi Mäenpää, "Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 971 – 987, July 2002.
  15. S. Zeenathunisa, A. Jaya and M. A. Rabbani,"An Intelligent Agent for Recognizing Face Under Dim Light Conditions", In the Proceedings of 2nd IEEE International Conference on Intelligent Agent and Multi-Agent Systems (IAMA 2011), pp. 43, September 2011.
  16. S. Zeenathunisa, A. Jaya and M. A. Rabbani, "A Biometric Approach towards Recognizing Face In Various Dark Illuminations", In the Proceedings of 1st IEEE International Conference on Electronics Communication and Computing Technologies (ICECCT '11), pp. 59, September 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Next Generation Face Recognition System(ngfrs) Illumination Variation Feature Extractor Lbp K-nn Classifier