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A Comparative Study of Face Authentication using Extreme Learning Machine, Euclidean and Mahalanobis Distance Classification Methods

S. Sruthi, M. Arun Kumar, S. Valarmathy

Abstract


Face recognition can be used for both verification and
identification. Today face recognition technology is being used to
combat passport fraud, support law enforcement, identify missing
children, and minimize benefit or identity fraud. The two main steps
in a face recognition system are: (i) to define an effective
representation of the face images, which includes sufficient
information of the face for future classification, (ii) to classify a new
face image with the chosen representation. In this paper, Extreme
Learning Machine method for face recognition is proposed and
compared with Euclidean and Mahalanobis distance methods for
better face recognition rate. The Mahalanobis distance is
a metric which is better adapted than the usual Euclidean distance to
settings involving non spherically symmetric distribution, where as
extreme learning machine (ELM) is an efficient learning algorithm
for generalized single hidden layer feed forward networks (SLFNs),
which performs well in classification applications. This will further
enhance the quality of facial image authentication. Various
experiments are done for 400 samples from ORL database for the
three methods and the results are analyzed.


Keywords


Eigenfaces, Extreme Learning Machine, Principal Component Analysis (PCA), Mahalanobis Distance.

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References


Alex Pentland, Baback Moghaddam, and Thad Starner, ―Vision Based

and Modular Eigen spaces for Face Recognition‖, IEEE conf., on

Computer Vision and Pattern Recognition, MIT Media Laboratory Tech.

Report no 245. 1994.

Anil K.Jain, Robert P.W.Duin. Statistical pattern Recognition: A

Review. IEEE Trans. Pattern Anal. Mach. Intell., vol.22, no.1, 2000

Chengliang Wang, Libin Lan, Yuwei Zhang, Minjie Gu Face

Recognition Based on Principal component Analysis and Support Vector

Machine, May 2011

Diamantaras and S. Y. Kung, ―Principal Component Neural Networks:

Theory and Applications‖, John Wiley & Sons, Inc., 1996.

Grudin M.A., ―On Internal Representations in Face Recognition

Systems,‖ Pattern Recognition, vol. 33, no. 7, pp. 1161-1177, 2000.

Huang,et al., ―Extreme Learning Machine: Theory and Applications,‖

Neurocomputing, vol. 70, pp.489-501, 2006.

Huang, et al., ―Convex Incremental Extreme Learning Machine,‖

Neurocomputing, vol. 70, pp.3056-3062, 2007.

Huang, et al., ―Universal Approximation Using Incremental Networks

with Random Hidden Computational Nodes‖, IEEE Transactions on

Neural Networks, vol. 17, no. 4, pp. 879-892, 2006.

Huang, et al., ―Can Threshold Networks Be Trained Directly?‖ IEEE

Transactions on Circuits and Systems II, vol. 53, no. 3, pp. 187-191,

Huang, et al., ―Real-Time Learning Capability of Neural Networks‖,

IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 863-878,

Ian Craw, Member IEEE Computer Society, Nicholuas Costen, Takashi

Kato, and Shigeru Akamatsu, Member IEEE Computer Society, ― How

Should We Represents Faces for Automatic Recognition‖, IEEE

Transaction on Pattern Analysis and Machine Intelligence, Vol- 21, No.

, PP 725— 735, 1999.

Jian Yang, David Zhang, Alejandro F. Frangi,and Jing-yu Yang, Two-

Dimensional PCA: A New Approach to Appearance-Based Face

Representation and Recognition, IEEE transactions on pattern analysis

and Machine intelligence, vol. 26, no. 1, January 2004

Kirby M. And Sirovich L., ―Application of the KLProcedure for the

Characterization of Human Faces,‖ IEEE Trans. Pattern Analysis and

Machine Intelligenxce, vol. 12, no. 1, pp. 103- 108, Jan. 1990.

Li,etal.,―Fully complex extreme learning Machine,‖ Neurocomputing,

vol. 68, pp. 306- 314, 2005.

Liang, et al., ―A Fast and Accurate On-line Sequential Learning

Algorithm for Feed forward Networks IEEE Transactions on Neural

Networks, vol. 17, no. 6, pp. 1411-1423, 2006.

Pentland.A, ―Looking at People: Sensing for Ubiquitous and Wearable

Computing,‖ IEEE Trans. Pattern Analysis and Machine Intelligence,

vol. 22, no. 1, pp. 107-119, Jan.2000.

Turk M. And Pentland A., ―Eigenfaces for Recognition,‖ J. Cognitive

Neuroscience, vol. 3, no. 1, pp. 71-86,1991.

Rafael C. Gonzalez and Richard E. Woods.―Digital image processing‖,

Second Edition, published by Pearson Education, 2003.

Sanguansat, W. Asdornwised, S. Jitapunkul S.Marukatat, twodimensional

linear discriminant analysis of principal component Vectors

for face recognition, IEEE, 2006.

Sirovich L.and Kirby M. ―Low-Dimensional Procedure for

Characterization of Human Faces,‖ J. ptical Soc. Am., vol. 4, pp. 519-

, 1987.

Zhu, et al., ―Evolutionary Extreme Learning Machine‖, Pattern

Recognition, vol. 38, no. 10, pp.1759-1763, 2005.

Zhao, Chellappa. Face Recognition: A Literature Survey. ACM

Computing Surveys, Vol.35, No.4, 2003, pp.399-458.

Zhao and Y. Yang, ―Theoretical Analysis of Illumination in PCA-Based

Vision Systems,‖ Pattern Recognition, vol. 32, no. 4, pp. 547-564, 1999.


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