Паралелно сравнително разпознаване на лица при идеални и не идеални условия с Hidden Markov Model (HMM) и Sparse разпознаване
Ключови думи:
разпознаване на лица, идеални и не идеални условия
Абстракт
Тази статия има за цел да сравни разпознаването на лица в идеални и не идеални условия с Hidden Markov Model (HMM) и двумерно матрично (sparse) разпознаване. Веднъж се извършва паралелно разпознаване с тези два метода в идеални условия и веднъж при наличие на генерирани три смущаващи фактори с различни параметри към базата данни. Направено е сравнение относно разпознаването [в % ].
Ключови думи: разпознаване на лица, идеални и не идеални условия
Литература
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[2] Yong Xu, Xiaozhao Fang, Jane You, Yan Chen, Hong Liu:
Noise-free representation based classification and face recognition experiments. Neurocomputing 147, pp. 307-314, 2015
[3] M. Oravec. Face Recognition. Face Recognition in Ideal and Noisy Conditions Using Support Vector Machines, PCA and LDA.
ISBN 978-953-307-060-5, InTech, April 2010
[4] L. Zhuang , T. Chan , A. Yang , S. Sastry, Y. Ma. Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment. International Journal of Computer Vision, pp. 1-24, February, 2014
[5] X. Peng, L. Zhang, Z. Yi, K. Tan. Learning Locality-Constrained Collaborative Representation for Robust Face Recognition. Pattern Recognition 47 (9), 2794-2806, 2014.
[6]. J. Wright, A. Yang, A. Ganesh, S. Sastry, Y. Ma. Robust Face Recognition via Sparse Representation. IEEE Transaction on Pattern Analysis and Machine Inteligence, Vol. 31, No.2, pp. 1-17, February 2009
[7] J. Chintal , Prof. S. Mishra. Investigating the Possibility of Recognizing the Forgery by Using Spatial & Transform Domain. International Journal of Advance Research in Computer Science and Management Studies,Vol. 3, May 2015
[2] Yong Xu, Xiaozhao Fang, Jane You, Yan Chen, Hong Liu:
Noise-free representation based classification and face recognition experiments. Neurocomputing 147, pp. 307-314, 2015
[3] M. Oravec. Face Recognition. Face Recognition in Ideal and Noisy Conditions Using Support Vector Machines, PCA and LDA.
ISBN 978-953-307-060-5, InTech, April 2010
[4] L. Zhuang , T. Chan , A. Yang , S. Sastry, Y. Ma. Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment. International Journal of Computer Vision, pp. 1-24, February, 2014
[5] X. Peng, L. Zhang, Z. Yi, K. Tan. Learning Locality-Constrained Collaborative Representation for Robust Face Recognition. Pattern Recognition 47 (9), 2794-2806, 2014.
[6]. J. Wright, A. Yang, A. Ganesh, S. Sastry, Y. Ma. Robust Face Recognition via Sparse Representation. IEEE Transaction on Pattern Analysis and Machine Inteligence, Vol. 31, No.2, pp. 1-17, February 2009
[7] J. Chintal , Prof. S. Mishra. Investigating the Possibility of Recognizing the Forgery by Using Spatial & Transform Domain. International Journal of Advance Research in Computer Science and Management Studies,Vol. 3, May 2015
Публикуван
2018-05-20
Как да се цитира
Petrova, P. (2018). Паралелно сравнително разпознаване на лица при идеални и не идеални условия с Hidden Markov Model (HMM) и Sparse разпознаване. КОМПЮТЪРНИ НАУКИ И КОМУНИКАЦИИ, 5(1), 60-65. изтеглен на от https://csc.bfu.bg/index.php/CSC/article/view/76
Раздел
Компютърни науки и комуникации - рецензирани публикации. ISSN: 1314-7846
Copyright (C) 2018 Petya Petrova
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
