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JCR Impact Factor: 1.102
JCR 5-Year IF: 0.734
Issues per year: 4
Current issue: Aug 2020
Next issue: Nov 2020
Avg review time: 70 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2016 - 15
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Face Recognition Performance Improvement using a Similarity Score of Feature Vectors based on Probabilistic Histograms

SRIKOTE, G. See more information about SRIKOTE, G. on SCOPUS See more information about SRIKOTE, G. on IEEExplore See more information about SRIKOTE, G. on Web of Science, MEESOMBOON, A. See more information about MEESOMBOON, A. on SCOPUS See more information about MEESOMBOON, A. on SCOPUS See more information about MEESOMBOON, A. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,261 KB) | Citation | Downloads: 472 | Views: 1,637

Author keywords
gaussian mixture model, expectation-maximization algorithm, similarity score, probabilistic histogram, face recognition

References keywords
recognition(12), face(10), pattern(7), vision(6), image(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-08-31
Volume 16, Issue 3, Year 2016, On page(s): 107 - 112
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.03015
Web of Science Accession Number: 000384750000015
SCOPUS ID: 84991066571

Abstract
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Full text preview
This paper proposes an improved performance algorithm of face recognition to identify two face mismatch pairs in cases of incorrect decisions. The primary feature of this method is to deploy the similarity score with respect to Gaussian components between two previously unseen faces. Unlike the conventional classical vector distance measurement, our algorithms also consider the plot of summation of the similarity index versus face feature vector distance. A mixture of Gaussian models of labeled faces is also widely applicable to different biometric system parameters. By comparative evaluations, it has been shown that the efficiency of the proposed algorithm is superior to that of the conventional algorithm by an average accuracy of up to 1.15% and 16.87% when compared with 3x3 Multi-Region Histogram (MRH) direct-bag-of-features and Principal Component Analysis (PCA)-based face recognition systems, respectively. The experimental results show that similarity score consideration is more discriminative for face recognition compared to feature distance. Experimental results of Labeled Face in the Wild (LFW) data set demonstrate that our algorithms are suitable for real applications probe-to-gallery identification of face recognition systems. Moreover, this proposed method can also be applied to other recognition systems and therefore additionally improves recognition scores.


References | Cited By  «-- Click to see who has cited this paper

[1] W. Zhao, R. Chellappa, P. J. Phillips and A. Rosenfeld, "Face recognition: A literature surve," ACM Comput Surv , vol. 35, no. 4, pp. 399-458, 2003,
[CrossRef]


[2] A. S. Georghiades, P. N. Belhumeur and D. J. Kriegman, "From few to many: Illumination cone models for face recognition under variable lighting and pose," IEEE Trans Pattern Anal Mach Intell, vol. 23, no. 6, pp. 643-660, 2001,
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[3] R. Basri and D. W. Jacobs, "Lambertian reflectance and linear subspaces," IEEE Trans Pattern Anal Mach Intell , vol. 25, no. 2, pp. 218-233, 2003,
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[6] M. Jian and K. M. Lam, "Simultaneous Hallucination and recognition of low-resolution faces based on singular value decomposition," IEEE Trans Circuits and Systems for Video Technology, vol. 25, no. 11, pp. 1761-1772, 2015,
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[8] G. B. Huang, V. Jain and E. L. Miller, "Unsupervised joint alignment of complex images," In: Proceedings of International Conference on Computer Vision, pp. 1-8, 2007,
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[9] Y. Wong, S. Chen, S. Mau, C. Sanderson and B. C. Lovell, "Multi-region probabilistic histograms for robust and scalable identity inference," In: Proceedings of International Conference on Biometrics, Lecture Notes in Computer Science (LNCS), pp. 199-10, 2009,
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[CrossRef]


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References Weight

Web of Science® Citations for all references: 20,173 TCR
SCOPUS® Citations for all references: 27,330 TCR

Web of Science® Average Citations per reference: 917 ACR
SCOPUS® Average Citations per reference: 1,242 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-09-27 12:10 in 118 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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