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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] 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, [CrossRef] [Web of Science Times Cited 3514] [SCOPUS Times Cited 4252] [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, [CrossRef] [Web of Science Times Cited 1121] [SCOPUS Times Cited 1392] [4] Y. Gao and M. K. Leung, "Face recognition using line edge map," IEEE Trans Pattern Anal Mach Intell , vol. 24, no. 6, pp. 764-779, 2002, [CrossRef] [Web of Science Times Cited 298] [SCOPUS Times Cited 385] [5] J. Soldera, C. A. R. Behaine and J. Scharcanski, "Customized orthogonal locality preserving projection with soft-margin maximization for face recognition," IEEE Trans Instrumentation and Measurement, vol. 64, no. 9, pp. 2417-2426, 2015, [CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 44] [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, [CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 115] [7] R. Brunelli and T. Poggio, "Face recognition: Features versus Template," IEEE Trans Pattern Anal Mach Intell, vol. 15, no. 10, pp. 1042-1052, 1993, [CrossRef] [Web of Science Times Cited 1340] [SCOPUS Times Cited 1886] [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, [CrossRef] [Web of Science Times Cited 154] [SCOPUS Times Cited 272] [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, [CrossRef] [SCOPUS Times Cited 240] [10] P. A. Viola and M. J. Jones, "Robust real-time face detection," International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004, [CrossRef] [Web of Science Times Cited 8244] [SCOPUS Times Cited 11021] [11] S. Zafeiriou, C. Zhang and Z. Zhang, "A survey of face detection in the wild: Past, present and future," Computer Vision and Image Understanding, vol. 138, pp. 1-24, 2015, [CrossRef] [Web of Science Times Cited 256] [SCOPUS Times Cited 364] [12] R. Gonzales and R. Woods. Digital Image Processing. 3rd ed. New Jersey: Prentice Hall, pp. 472-473, 2007. [13] C. Bishop. Pattern Recognition and Machine Learning. Berlin: Springer, pp. 435-439, 2006. [14] E. Nowak, F. Jurie and B. Triggs, "Sampling strategies for bag-of-features image classification," In: European Conference Computer Vision (ECCV), Part IV, Lecture Notes in Computer Science (LNCS), pp. 490-503, 2006, [CrossRef] [SCOPUS Times Cited 651] [15] C. Sanderson and B. C. Lovell, "Multi-region probabilistic histograms for robust and scalable identity inference," In: International Conference on Biometrics, Lecture Notes in Computer Science (LNCS), pp. 199-208, 2009, [CrossRef] [16] F. Bashar, A. Khan, F. Ahmed and H. Kabir, "Face recognition using similarity pattern of image directional edge response," Advances in Electrical and Computer Engineering, pp. 69-76, 2014, [CrossRef] [Full Text] [Web of Science Times Cited 7] [SCOPUS Times Cited 12] [17] P. N. Tan, M. Steinbach and V. Kumar. Introduction to Data Mining. Boston: Pearson Addison Wesley, p. 73, 2006. [18] G. B. Huang, M. Ramesh, T. Berg and E. L. Miller. Labeled Faces in the Wild:A database for studying face recognition in unconstrained environments. Faces in Real-Life Images Workshop in European Conference on Computer Vision (ECCV), 2008. [19] E. Nowak and F. Jurie, "Learning visual similarity measures for comparing never seen objects," In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2007, [CrossRef] [Web of Science Times Cited 66] [SCOPUS Times Cited 141] [20] S. Chen, S. Mau, M. T. Harandi, C. Sanderson, A. Bigdeli and B. C. Lovell, "Face recognition from still images to video sequences: A local-feature-based framework," EURASIP Journal on Image and Video Processing, pp. 1-14, 2011, [CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 45] [21] M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991, [CrossRef] [Web of Science Times Cited 8573] Web of Science® Citations for all references: 23,740 TCR SCOPUS® Citations for all references: 20,820 TCR Web of Science® Average Citations per reference: 1,079 ACR SCOPUS® Average Citations per reference: 946 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 2024-12-01 13:19 in 116 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). 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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.
