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Face Recognition Performance Improvement using a Similarity Score of Feature Vectors based on Probabilistic HistogramsSRIKOTE, G.   , MEESOMBOON, A. |
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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
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. |
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[1] Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi, Rathour, Navjot, Khanam, Zeba, Gehlot, Anita, Singh, Rajesh, Rashid, Mamoon, AlGhamdi, Ahmed Saeed, Alshamrani, Sultan S., Applied Sciences, ISSN 2076-3417, Issue 22, Volume 11, 2021.
Digital Object Identifier: 10.3390/app112210540 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
