|3/2016 - 15|
Face Recognition Performance Improvement using a Similarity Score of Feature Vectors based on Probabilistic HistogramsSRIKOTE, G. , MEESOMBOON, A.
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gaussian mixture model, expectation-maximization algorithm, similarity score, probabilistic histogram, face recognition
recognition(12), face(10), pattern(7), vision(6), image(5)
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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
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|
| 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, |
 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 1895] [SCOPUS Times Cited 2510]
 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 645] [SCOPUS Times Cited 886]
 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 230]
 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 11] [SCOPUS Times Cited 12]
 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 9] [SCOPUS Times Cited 9]
 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 1086] [SCOPUS Times Cited 1448]
 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] [SCOPUS Times Cited 153]
 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 97]
 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 4458] [SCOPUS Times Cited 6393]
 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 30] [SCOPUS Times Cited 33]
 R. Gonzales and R. Woods. Digital Image Processing. 3rd ed. New Jersey: Prentice Hall, pp. 472-473, 2007.
 C. Bishop. Pattern Recognition and Machine Learning. Berlin: Springer, pp. 435-439, 2006.
 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 417]
 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,
 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 4] [SCOPUS Times Cited 5]
 P. N. Tan, M. Steinbach and V. Kumar. Introduction to Data Mining. Boston: Pearson Addison Wesley, p. 73, 2006.
 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.
 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 34] [SCOPUS Times Cited 108]
 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 8] [SCOPUS Times Cited 20]
 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 5998]
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