利用MRA-框架的人脸识别方法的研究
Research of Face Recognition Method Use of MRA-Framework

作者: 吴兆英 :陕西师范大学,陕西 西安; 李万社 , 马峰 :陕西师范大学,陕西 西安 ;

关键词: 人脸识别MRA-框架PCA加噪权重系数Face Recognition MRA-Framelet Principal Component Analysis Noise Adding Weight Coefficient

摘要: 图像拼接中出现的运动目标可能使拼接出现不能正常拼接或者拼接出多重影像的现象。本文提出一种图像拼接的运动目标检传统的PCA方法,人脸图像加噪后,人脸识别率会明显下降。本文针对这种情况,分别利用正交小波 + PCA和小波框架 + PCA方法进行了研究.首先对人脸图像进行加噪处理,然后对图像进行正交小波和小波框架分解,进而对分解后的子图分别利用PCA方法进行降维和特征提取,最后用三阶近邻法作为分类器进行分类识别。通过ORL人脸数据库的验证,结果证明了本文方法的有效性,很好的提高了加噪情况下人脸图像的识别率。

Abstract: After adding noise, face recognition rate of the traditional PCA method will be significantly lowered. This paper will use methods of orthogonal wavelet + PCA and wavelet frame + PCA to study it respectively. First, we add noise to deal with the image, then decompose the image under the use of orthogonal wavelet and wavelet frame; next, for the subgraph that has decomposed we will reduce the dimensionality and feature extraction using PCA method respectively; finally, we use third-order nearest neighbor as the classifier to classify and identify it. Through the test and veri-fication of the ORL face database, it shows the effectiveness of this method, which is a good way to improve the recognition rate of face image under the condition of adding noise.

文章引用: 吴兆英 , 李万社 , 马峰 (2016) 利用MRA-框架的人脸识别方法的研究。 建模与仿真, 5, 1-8. doi: 10.12677/MOS.2016.51001

参考文献

[1] 周杰, 卢春雨, 张长水, 等. 人脸自动识别方法综述[J]. 电子学报, 2000, 28(4): 102-106.

[2] Javed, A. (2013) Face Recognition Based on Principal Component Analysis. The Journal of New Industrialization, 5, 38.

[3] Yang, X.Y., Shi, Y. and Zhou, W.L. (2011) Construction of Parameterizations of Masks for Tight Wavelet Frames with Two Symmetric/Antisymmetric Generators and Applications in Image Compression and Denoising. Journal of Computational and Applied Mathematics, 235, 2112-2136. http://dx.doi.org/10.1016/j.cam.2010.10.009

[4] Chui, C.K. and He, W. (2000) Compactly Supported Tight Frames Associated with Refinable Functions. Applied and Computational Harmonic Analysis, 8, 293-319.

[5] Han, B. (2013) Matrix Splitting with Symmetry and Symmetric Tight Framelet Filter Banks with Two High-Pass Filters. Applied and Computational Harmonic Analysis, 35, 200-227.
http://dx.doi.org/10.1016/j.acha.2012.08.007

[6] Han, B. (2014) Symmetric Tight Framelet Filter Banks with Three High-pass Filters. Applied and Computational Harmonic Analysis, 37, 140-161.
http://dx.doi.org/10.1016/j.acha.2013.11.001

[7] Han, B. and Mo, Q. (2004) Splitting a Matrix of Laurent Poly-nomials with Symmetry and Its Application to Symmetric Framelets Filter Banks. SIAM Journal on Matrix Analysis and Applications, 26, 97-124.
http://dx.doi.org/10.1137/S0895479802418859

[8] Han, B. and Mo, Q. (2004) Tight Wavelet Frames Generated by Three Symmetric B-Spline Functions with High Vanishing Moments. Proceedings of the American Mathematical Society, 132, 77-86.
http://dx.doi.org/10.1090/S0002-9939-03-07205-8

[9] Han, B. and Mo, Q. (2005) Symmetric MRA Tight Frame with Three Generators and High Vanishing Moments. Applied and Computational Harmonic Analysis, 18, 67-93.
http://dx.doi.org/10.1016/j.acha.2004.09.001

[10] Hill, P.R., Anantrasirichai, N., Achim, A., Al-Mualla, M.E. and Bull, D.R. (2015) Undecimated Dual-Tree Complex Wavelet Transforms. Signal Processing: Image Communication, 35, 61-70.
http://dx.doi.org/10.1016/j.image.2015.04.010

[11] 周国民, 陈勇, 李国军. 人脸识别中应用小波变换的两个关键问题[J]. 浙江大学学报, 2005, 32(1): 34-38.

[12] Daubechies, I., Han, B., Ron, A. and Shen, Z.W. (2003) Framelets: MRA-Based Constructions of Wavelet Frames. Applied and Computational Harmonic Analysis, 14, 1-46.
http://dx.doi.org/10.1016/s1063-5203(02)00511-0

[13] Chui, C.K., He, W. and Stochler, J. (2002) Compactly Supported Tight and Sibling Frames with Maximum Vanishing Moments. Applied and Computational Harmonic Analysis, 13, 224-262.
http://dx.doi.org/10.1016/S1063-5203(02)00510-9

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