基于局部结构分解的人脸图像特征提取方法
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摘要
人脸识别作为一种关键的生物特征识别技术,目前仍有很多问题需要解决,例如人脸图像特征提取。本文在局部结构图像分解(IDLS)的基础上提出了一种人脸特征提取方法。利用线性回归模型描述局部图像窗口中心宏像素和近邻宏像素之间的结构关系,并得到了局部结构信息。根据图像结构信息,将图像分解成一系列结构图像,并对每个结构图像进行均匀下采样和归一化处理,得到了一个高维的特征向量。最后通过在NUST_RWFR人脸数据库上验证了基于IDLS的人脸图像特征提取方法的有效性,该方法具有较高的识别率,而且计算耗时问题可以通过压缩图像解决。
Face recognition is a key biometric identification technology,and there are still many problems to be solved such as face image feature extraction. This paper presents a robust but simple image feature extraction method,called image decomposition based on local structure( IDLS). It is assumed that in the local window of an image,the macro-pixel( patch) of the central pixel,and those of its neighbors,are locally linear. IDLS captures the local structural information by describing the relationship between the central macro-pixel and its neighbors. This relationship is represented with the linear representation coefficients determined using ridge regression. One image is actually decomposed into a series of sub-images( also called structure images) according to a local structure feature vector. All the structure images,after being down-sampled for dimensionality reduction,are concatenated into one super-vector. The proposed method is applied to face recognition and examined using our real-world face image database NUST-RWFR。This method has high recognition rate and the computation time consuming problem can be solved by compressing the image.
Face recognition is a key biometric identification technology,and there are still many problems to be solved such as face image feature extraction. This paper presents a robust but simple image feature extraction method,called image decomposition based on local structure( IDLS). It is assumed that in the local window of an image,the macro-pixel( patch) of the central pixel,and those of its neighbors,are locally linear. IDLS captures the local structural information by describing the relationship between the central macro-pixel and its neighbors. This relationship is represented with the linear representation coefficients determined using ridge regression. One image is actually decomposed into a series of sub-images( also called structure images) according to a local structure feature vector. All the structure images,after being down-sampled for dimensionality reduction,are concatenated into one super-vector. The proposed method is applied to face recognition and examined using our real-world face image database NUST-RWFR。This method has high recognition rate and the computation time consuming problem can be solved by compressing the image.
引文
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[2]M.Dantone,J.Gall,G.Fanelli,etal.Real-time facial feature detection using conditional regression forests[J].In Proc.IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2012:2578-2585.
[3]H.Han,S.Shan,X.Chen,etal.A comparative study on illumination preprocessing in face recognition[J].Pattern Recognition,2013,46(6):1691-1699.
[4]J.Yang,A.Frangi,J.Yang,etal.KPCA plus LDA:a complete kernel Fisher discriminant framework for feature extraction and recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(2):230-244.
[5]T.Ojala,M.Pietikainen,T.Maenpaa.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[6]T.Ahonen,A.Hadid,M.Pietikainen.Face description with local binary patterns:Application to face recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(12):2037-2041.
[7]X.Y.Tan,B.Triggs.Enhanced local texture feature sets for face recognition under difficult lighting conditions[J].IEEE Transactions on Image Processing,2010,19(6):1635-1650.
[8]W.Zhang,S.Shan,W.Gao,etal.Local Gabor binary pattern histogram sequence(LGBPHS):A novel non-statistical model for face representation and recognition[J].In Proc.International Conference on Computer Vision,2005,1:786-791.
[9]B.Zhang,S.Shan,X.Chen,etal.Histogram of Gabor phase patterns(HGPP):a novel object representation approach for face recognition[J].IEEE Transactions on Image Processing,2007,16(1):57-68.
[10]Z.Lei,S.Liao,M.Pietikainen,etal.Face recognition by exploring information jointly in space,scale and orientation[J].IEEE Transactions on Image Processing,2011,20(1):247-256.
[11]G.Lu,Z.Lin,Z.Jin.Face recognition using discriminant locality preserving projections based on maximum margin criterion[J].Pattern Recognition,2010,43(10):3572-3579.
[12]M.Guillaumin,T.Mensink,J.Verbeek,etal.Face recognition from caption-based supervision[J].International Journal of Computer Vision,2012,96(1):64-82.
[13]B.Zhang,Y.Gao,S.Zhao,etal.Local derivative pattern versus local binary pattern:face recognition with high-order local pattern descriptor[J].IEEE Transactions on Image Processing,2010,19(2):533-544.
[14]J.Qian,J.Yang,Y.Xu.Local structure-based image decomposition for feature extraction with applications to face recognition[J].IEEE Transactions on Image Processing,2013,22(9):3591-3603.
[15]H.Seo,P.Milanfar.Face verification using the lark representation[J].IEEE Transactions on Information Forensics and Security,2011,6(4):1275-1286.
[16]J.Qian,J.Yang.A novel feature extraction method for face recognition under different lighting conditions[J].Biometric Recognition.Springer Berlin Heidelberg,2011:17-24.
[17]田华,蒲天银.一种改进的ASM人脸特征点定位方法[J].重庆邮电大学学报(自然科学版),2014,26(1):124-130.
[18]M.Yang,L.Zhang,J.Yang,et al.Regularized robust coding for face recognition[J].IEEE Transactions on Image Processing,2013,22(5):1753-1766.