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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
 
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Download PDF pdficon (1,261 KB) | Citation | Downloads: 981 | Views: 2,737

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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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

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[CrossRef]


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References Weight

Web of Science® Citations for all references: 23,554 TCR
SCOPUS® Citations for all references: 20,600 TCR

Web of Science® Average Citations per reference: 1,071 ACR
SCOPUS® Average Citations per reference: 936 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 2024-07-11 23:51 in 116 seconds.




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Stefan cel Mare University of Suceava, Romania


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