基于图像重构和特征融合的人脸识别方法研究
|
摘要
人脸所反映的视觉信息在人与人的交流中有着重要的作用和意义,计算机人脸识别技术就是利用计算机分析人脸图像,从中提取有效的识别信息,用来辨认身份的一门技术,由于其广泛的应用领域,在近四十年里得到了广泛的关注和研究。
人脸识别技术主要包括两部分内容:特征提取和分类器设计。本文主要从人脸图像的特征提取算法入手,分别提出了几种新的基于图像重构和基于特征融合的人脸识别方法。实验证明本文提出的人脸识别方法能有效的提高人脸识别的准确率,具有一定的理论价值与实用价值。本文的研究工作主要包括以下几个方面:
1)基于图像重构的人脸识别方法:传统的基于特征子空间的人脸识别方法产生的子空间通常都是由人脸库中的所有训练样本产生的一个通用的子空间,此子空间包含的更多的是所有人脸样本的共性特征,而忽略了人脸的一些个性特征。本文提出了一种基于子空间和图像重构的人脸识别方法,该方法以单个人的训练样本集获取其人脸特征子空间,并将识别图像向每一个特征子空间中进行映射及重构,获得的图像最小重构误差作为判据实现人脸识别。同时,我们引入支持向量机(SVM)分类识别算法,将图像重构误差序列作为提取的图像特征参数进行分类识别,两种方法都取得了较好的实验结果。
2)基于多特征融合的人脸识别方法:通过对建立在统计学习理论基础上的SVM的研究,分析了SVM用于人脸识别的可行性,提出了基于多特征融合的人脸识别方法。该方法首先对预处理后的人脸图像进行全局特征及局部分量的提取,分别采用离散余弦变换(DCT)提取包含图像大量信息的低频部分特征和奇异值分解(SVD)抽取图像的代数特征作为图像的全局特征,采用非负矩阵分解(NMF)提取图像的局部分量特征,然后将此二类特征进行串接并以独立成份分析(ICA)提取组合特征的独立特征用于支持向量机进行人脸识别。
3)基于独立特征融合的人脸识别方法:DCT能有效的将高维的人脸图像转换到低维空间并保留图像可识别的大部分信息,适合于图像全局特征的提取,而Gabor小波变换适合于图像的局部特征及分类特征提取,这两种特征被广泛应用于人脸识别。基于这两种方法,我们引入ICA技术以提取图像的独立Gabor特征和独立DCT特征,然后将两者进行有效融合以获取新的独立特征,使其同时具有Gabor特征的局部信息和DCT特征的全局信息,并有效的降低特征向量的维数,去除冗余特征。最后,融合后的独立特征被用于SVM实现人脸分类识别。
4)基于HMM-SVM混合模型的人脸识别方法:由于HMM具有良好的时间序列建模能力,以及SVM在有限样本的分类方面的优良性能,本文提出了一种基于HMM-SVM混合模型的人脸识别方法。该方法首先将人脸图像以采样窗从上到下进行采样,分别采用DCT和SVD提取各个采样窗图像的特征参数并串接成一维观察向量,然后将每个人的训练图像的观察向量用于训练每个人的HMM模型,识别图像的观察向量用于求出其对应于每个HMM模型的输出概率,最后将输出概率用于SVM进行分类训练及识别测试。
5)人脸识别原型系统设计:以本文提出的人脸识别方法为基础,实现了人脸识别系统的关键功能模块,架构出自动人脸识别的原型系统,为今后开发产品级的人脸识别系统提供了可行性分析依据以及相应的技术储备。
The visual information of faces play a very important role in human communication. Face recognition technique tries to make the computer have the ability of identifying person by analyzing and extracting features from the face images. Because of its wide applications, the face recognition has been one of the hot research subjects in near four decades.
A mature face recognition process can be divided into two steps: feature representation and classification. The extraction of image features is one of the fundamental tasks in image recognition. From features extraction angle of view, we proposed some novel methods for face recognition. The computer simulations illustrate these methods enhance the recognition rate effectively and have theoretical and practical values. The main contributions of this thesis include:
1) Face recognition based on image reconstruction: Traditional subspace-based Face Recognition methods obtain a universal subspace by using all trained images. The subspace mainly represents the commonness of human faces but there are a few sights of the individuality owned by a single person's face. In this paper, based on subspace methods and image reconstruction, we present some novel methods for face recognition. When applied to face recognition, the fundamental difference between the traditional subspace methods and our methods is that we obtain the basis images by using each person's pictures respectively, while the traditional way uses the whole training images of the database. After the step above, we obtained the features which would be employed to reconstruct the images by mapping the test images to the basis images. And then we use two ways for face recognition, the first way is adopting the minimum reconstruction error and the second is employing support vector machine (SVM) by using the reconstruction error vectors. Finally, experiments based on three different databases illustrate the effectivity of these methods.
2)Face Recognition Based on Features Fusion: Support vector machine (SVM) is a new machine learning method that is established on the statistics learning theories, and in this paper, we propose a novel algorithm for facial recognition based on features fusion in support vector machine (SVM). First, some local features and global features from pre-processed face images are obtained. The global features are obtained by making use of discrete cosine transform (DCT) and singular value decomposition (SVD). At the same time, the local features by utilizing non-negative matrix factorization (NMF) are also obtained. Furthermore, the feature vectors fused by independent component analysis (ICA) with global and local features are given. Finally, the feature vectors are used to train SVM to realize the face recognition, and the computer simulation illustrates the effectivity of this method on the ORL face database.
3) Independent Features Fusion for Face Recognition: As a holistic feature extraction method, the DCT converts high-dimensional face images into low-dimensional spaces in which more significant facial features are maintained. On the contrary, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale and orientation selectivity, which produce salient local features which are most suitable for face recognition. So, we use Gabor wavelets for the local features and then integrate Gabor features with DCT coefficients. In addition to, because ICA would reduce redundant features and represent more explicitly the independent features which are most useful for subsequent pattern discrimination and associative recall, ICA has been used for extracting their independent features respectively. And then, the independence property of the independent Gabor features and DCT features leads to the application of the support vector machine (SVM) for classification.
4) Face Recognition Based on HMM-SVM: Because SVM has excellent ability to classify and HMM has good ability to time sequence modeling, we used a mixed model based on HMM and SVM for face recognizing. In this method, a sequence of overlapping sub-images is extracted from each face image by using discrete cosine transform (DCT) and singular value decomposition (SVD). Afterward, the sequence which is extracted from training images is modeled by using HMM, and then, the output probability of each HMM for the training sequence has been considered as the input vector of SVM for its training. In the end, the output probability of each HMM for the testing sequences has been considered as the input vector of SVM for its testing. The computer simulation illustrates the effectivity of this method on the Olivetti research laboratory (ORL) database.
5) Face recognition prototype system design: Based on these novel algorithms, we discuss the process of face recognition and introduce the function and implementation modules, and then develope the face recognition prototype system. All these results lay a foundation for a business version in the future.
人脸识别技术主要包括两部分内容:特征提取和分类器设计。本文主要从人脸图像的特征提取算法入手,分别提出了几种新的基于图像重构和基于特征融合的人脸识别方法。实验证明本文提出的人脸识别方法能有效的提高人脸识别的准确率,具有一定的理论价值与实用价值。本文的研究工作主要包括以下几个方面:
1)基于图像重构的人脸识别方法:传统的基于特征子空间的人脸识别方法产生的子空间通常都是由人脸库中的所有训练样本产生的一个通用的子空间,此子空间包含的更多的是所有人脸样本的共性特征,而忽略了人脸的一些个性特征。本文提出了一种基于子空间和图像重构的人脸识别方法,该方法以单个人的训练样本集获取其人脸特征子空间,并将识别图像向每一个特征子空间中进行映射及重构,获得的图像最小重构误差作为判据实现人脸识别。同时,我们引入支持向量机(SVM)分类识别算法,将图像重构误差序列作为提取的图像特征参数进行分类识别,两种方法都取得了较好的实验结果。
2)基于多特征融合的人脸识别方法:通过对建立在统计学习理论基础上的SVM的研究,分析了SVM用于人脸识别的可行性,提出了基于多特征融合的人脸识别方法。该方法首先对预处理后的人脸图像进行全局特征及局部分量的提取,分别采用离散余弦变换(DCT)提取包含图像大量信息的低频部分特征和奇异值分解(SVD)抽取图像的代数特征作为图像的全局特征,采用非负矩阵分解(NMF)提取图像的局部分量特征,然后将此二类特征进行串接并以独立成份分析(ICA)提取组合特征的独立特征用于支持向量机进行人脸识别。
3)基于独立特征融合的人脸识别方法:DCT能有效的将高维的人脸图像转换到低维空间并保留图像可识别的大部分信息,适合于图像全局特征的提取,而Gabor小波变换适合于图像的局部特征及分类特征提取,这两种特征被广泛应用于人脸识别。基于这两种方法,我们引入ICA技术以提取图像的独立Gabor特征和独立DCT特征,然后将两者进行有效融合以获取新的独立特征,使其同时具有Gabor特征的局部信息和DCT特征的全局信息,并有效的降低特征向量的维数,去除冗余特征。最后,融合后的独立特征被用于SVM实现人脸分类识别。
4)基于HMM-SVM混合模型的人脸识别方法:由于HMM具有良好的时间序列建模能力,以及SVM在有限样本的分类方面的优良性能,本文提出了一种基于HMM-SVM混合模型的人脸识别方法。该方法首先将人脸图像以采样窗从上到下进行采样,分别采用DCT和SVD提取各个采样窗图像的特征参数并串接成一维观察向量,然后将每个人的训练图像的观察向量用于训练每个人的HMM模型,识别图像的观察向量用于求出其对应于每个HMM模型的输出概率,最后将输出概率用于SVM进行分类训练及识别测试。
5)人脸识别原型系统设计:以本文提出的人脸识别方法为基础,实现了人脸识别系统的关键功能模块,架构出自动人脸识别的原型系统,为今后开发产品级的人脸识别系统提供了可行性分析依据以及相应的技术储备。
The visual information of faces play a very important role in human communication. Face recognition technique tries to make the computer have the ability of identifying person by analyzing and extracting features from the face images. Because of its wide applications, the face recognition has been one of the hot research subjects in near four decades.
A mature face recognition process can be divided into two steps: feature representation and classification. The extraction of image features is one of the fundamental tasks in image recognition. From features extraction angle of view, we proposed some novel methods for face recognition. The computer simulations illustrate these methods enhance the recognition rate effectively and have theoretical and practical values. The main contributions of this thesis include:
1) Face recognition based on image reconstruction: Traditional subspace-based Face Recognition methods obtain a universal subspace by using all trained images. The subspace mainly represents the commonness of human faces but there are a few sights of the individuality owned by a single person's face. In this paper, based on subspace methods and image reconstruction, we present some novel methods for face recognition. When applied to face recognition, the fundamental difference between the traditional subspace methods and our methods is that we obtain the basis images by using each person's pictures respectively, while the traditional way uses the whole training images of the database. After the step above, we obtained the features which would be employed to reconstruct the images by mapping the test images to the basis images. And then we use two ways for face recognition, the first way is adopting the minimum reconstruction error and the second is employing support vector machine (SVM) by using the reconstruction error vectors. Finally, experiments based on three different databases illustrate the effectivity of these methods.
2)Face Recognition Based on Features Fusion: Support vector machine (SVM) is a new machine learning method that is established on the statistics learning theories, and in this paper, we propose a novel algorithm for facial recognition based on features fusion in support vector machine (SVM). First, some local features and global features from pre-processed face images are obtained. The global features are obtained by making use of discrete cosine transform (DCT) and singular value decomposition (SVD). At the same time, the local features by utilizing non-negative matrix factorization (NMF) are also obtained. Furthermore, the feature vectors fused by independent component analysis (ICA) with global and local features are given. Finally, the feature vectors are used to train SVM to realize the face recognition, and the computer simulation illustrates the effectivity of this method on the ORL face database.
3) Independent Features Fusion for Face Recognition: As a holistic feature extraction method, the DCT converts high-dimensional face images into low-dimensional spaces in which more significant facial features are maintained. On the contrary, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale and orientation selectivity, which produce salient local features which are most suitable for face recognition. So, we use Gabor wavelets for the local features and then integrate Gabor features with DCT coefficients. In addition to, because ICA would reduce redundant features and represent more explicitly the independent features which are most useful for subsequent pattern discrimination and associative recall, ICA has been used for extracting their independent features respectively. And then, the independence property of the independent Gabor features and DCT features leads to the application of the support vector machine (SVM) for classification.
4) Face Recognition Based on HMM-SVM: Because SVM has excellent ability to classify and HMM has good ability to time sequence modeling, we used a mixed model based on HMM and SVM for face recognizing. In this method, a sequence of overlapping sub-images is extracted from each face image by using discrete cosine transform (DCT) and singular value decomposition (SVD). Afterward, the sequence which is extracted from training images is modeled by using HMM, and then, the output probability of each HMM for the training sequence has been considered as the input vector of SVM for its training. In the end, the output probability of each HMM for the testing sequences has been considered as the input vector of SVM for its testing. The computer simulation illustrates the effectivity of this method on the Olivetti research laboratory (ORL) database.
5) Face recognition prototype system design: Based on these novel algorithms, we discuss the process of face recognition and introduce the function and implementation modules, and then develope the face recognition prototype system. All these results lay a foundation for a business version in the future.
引文
[1]王蕴红,谭铁牛.现代身份鉴别新技术-生物特征识别技术.中国基础科学.科学前沿,2000,9:4-10.
[2]包桂秋,林喜荣,苏晓生,周兆英.基于人体生物特征的身份鉴别技术发展概况.清华大学学报(自然科学版),2001,41(4):72-76.
[3]山世光.人脸识别中若干关键问题的研究:(博士学位论文).北京中国科学院,2004.
[4]http://www.biometricgroup.com/reports/public/market_report.html
[5]Chellappa R et al.Human and Machine Recognition of Faces:A Survey[J].Proceedings of the IEEE,1995,83(5):705-740.
[6]周杰,卢春雨,张长水,李衍达.人脸自动识别方法综述.电子学报,2000,5(4):102-106.
[7]张翠平,苏光大.人脸识别技术综述.中国图像图形学报,2001,5(11):885-894。
[8]周激流,张晔.人脸识别理论研究进展[J].计算机辅助设计与图形学学报,1999,11(2):180-184.
[9]肖兵,王英辉.人脸识别研究综述.陕西师范大学学报2005.8:1-5.
[10]Andrea F A,Michele N,Daniel R and Gabriele S,2D and 3D face recognition:A survey,Pattern Recognition Letters,2007,28(14):1885-1906
[11]段锦,周春光,刘小华.三维人脸识别研究进展.小型微型计算机系统,2004,25(5):886-890.
[12]刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法.自动化学报,2003,29(6):900-911.
[13]Bledsoe W W.Man Machine Facial Recognition[R].Palo Alto CA,1966.
[14]Kelly M D.Visual identification of people by computer.Tech.Rep.AI-130,Stanford AI Project,Stanford,CA,1970.
[15]Brunelli R,Poggio T.Feature recognition:Feature versus templates.IEEE Transactions on PAMI,1993,15(10):1024-1052.
[16]Roeder N,Li X B.Accuracy analysis for facial feature detection.Pattern Recognition,1996,29(1):143-157.
[17]Laurenz W,Jean M F,Norbert K,et al.Face Recognition by Elastic Graph Matching [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):775-779.
[18]Lades M,Vorbuggen J,Buhmann J.Distortion invariant object recognition in the dynamic link architecture.IEEE Trans.Computers,1993,42(3):300-311.
[19]Wiskott L,Fellous J M,Kruger N,Malsburg C V.Face recognition by elastic bunch graph matching.IEEE Trans.PAMI,1997,19(7):775-779.
[20]Lee M W,Ranganath S.Pose-invariant face recognition using a 3D deformable model.Pattern Recognition,2003,36(8):1835-1846.
[21]薛理立,艾海舟.基于弹性图匹配的实时视频流人脸识别.计算机科学,2003(11).
[22]尹宝才,张壮,孙艳丰,王成章.基于三维形变模型的多姿态人脸识别.北京工业大学学报,2007(3).
[23]山世光,高文等.基于纹理分布和变形模板的面部特征提取.软件学报,2001,12(4):570-577.
[24]Kirby M,Sirovich L.Application of the KL procedure for the characterization of human faces,IEEE Trans.Pattern and Machine Intelligence,1990,12(1):103-108
[25]Turk M A,Pentland A P.Eigenfaces for recognition.Journal of Cognitive Neuroscience,1991,3(1):71-86
[26]Zhang J,Yan Y,Lades M.Face recognition:Eigenfaces,elastic Matching,and neural nets.IEEE Proc,1997,85(9):1423-1435.
[27]Lanitis A,Taylor C J,Cootes T F.Automatic interpretation and coding of face images using flexible models.IEEE Trans.PAMI,1997,19(7):743-756.
[28]Pentland A,Moghaddatn B,Starner T.View-based and modular eigenspaces for face recognition.IEEE Proc.CVPR,1994:84-91.
[29]Liu C J,Wechsler H.Probabilistic reasoning model for face recognition.IEEE Proc.CVPR,1998:827-832.
[30]羊牧,周激流,胡艳梅.基SVD和KL投影相融合的人脸识别算法.成都信息工程学院学报,2007,22(5):602-605.
[31]Moghaddatn B,Jebara T,Pentland A.Bayesian face recognition.Pattern Recognition,2000,33(11):1771-1782
[32]杨琼,丁晓青.对称主分量分析及其在人脸识别中的应用.计算机学报,2003.26:9-15.
[33]彭辉,张长水,荣钢,等.基于K-L变换的人脸识别自动识别方法[J],清华大学学报(自然科学版),1997,37(3):67-70.
[34]陈刚,戚飞虎.实用人脸识别系统的本征脸法实现.计算机研究与发展,2001(2)
[35]高全学,潘泉,梁彦,张洪才,程咏梅.基于描述特征的人脸识别研究.自动化学报,2006(3).
[36]Klema V C.The singular value decomposition:Its computation and some applications.IEEE Transaction son Automatic Control,1980,25:164- 176.
[37]Hong Z Q.Algebraic feature extraction of image recognition.Recognition.1991,24(3):211-219.
[38]Cheng Y Q,Yang J Y.A Novel Feature Extraction Method for Image Recognition Based On Similar Oiscriminant Function.Pattern Recognition,1993,26(1):115-125.
[39]Zhao H,Yuen P C,Incremental Linear Discriminant Analysis for Face Recognition,IEEE Transactions on.Systems,Man,and Cybernetics,Part B.2008,38(1):210-221.
[40]Zuo W M,Zhang D,Yang J,Wang K Q.BDPCA plus LDA:a novel fast feature extraction technique for face recognition,IEEE Transactions on.Systems,Man,and Cybernetics,Part B.2006,36(4):946-953.
[41]Xiang C,Fan X A,Lee T H.Face recognition using recursive Fisher linear discriminant,Image Processing,IEEE Transactions on.2006,15(8):2097-2105.
[42]Zheng W S,Lai J H,Yuen P C.GA-fisher:a new LDA-based face recognition algorithm with selection of principal components.IEEE Transactions on.Systems,Man,and Cybernetics,Part B.2005,35(5):1065- 1078.
[43]Liu Q S,Lu H Q,Ma S D.Improving kernel Fisher discriminant analysis for face recognition.Circuits and Systems for Video Technology,IEEE Transactions on,2004,14(1):42-49.
[44]Common P.Independent component analysis:A new concept.Signal Processing,1994,36(3):287-314.
[45]Bartlett M S,Lades H M,Sejnowski T J.Independent component representations for face recognition.SPIE Proc.1998,5,28-64.
[46]Kim T K,Kim H,Hwang W,Kittler J.Independent component analysis in a local residue space for face recognition.Pattern Recognition,2004,37(9):1873-1885.
[47]Deniz O,Castrillon M,Hernandez M.Face recognition using independent component analysis and support vector machines.Pattern Recognition Letters,2003,24(13):2153-2157.
[48]Liu C J,Wechsler H.Comparative assessment of independent component analysis(ICA)for face recognition.In:Proceedings of International Conference on Audio and Video Based Biometric Person Authentication,USA,Washington DC,1999.
[49]杨竹青,李勇,胡德文.独立成分分析方法综述[J].自动化学报,2002,28(5):762-772.
[50]Hyvarinen A.Fast and robust fixed-point algorithms for independent component analysis,IEEE Trans.Neural Networks,1999,10:626-634.
[51]Lee D D,Seung H S.Learning the parts of objects by non-negative matrix factorization [J].Nature,1999,401(10):788-791.
[52]David G,Jordi V.Non-negative Matrix Factorization for Face Recognition.Lecture Notes in Computer Science,2002,25(4):336-344.
[53]Li S Z,Hou X W,Zhang H J,et al.Learning Spatially Localized,Part-based Representation.IEEE Conf.on Computer Vision and Pattern Recognition,2001,1:207-212.
[54]Feng T,Li S Z,Shum H,et al.Local Non-Negative Matrix Factorization as a Visual Representation.Proceedings of The 2nd International Conference on Development and Learning,2002.
[55]Liu Q S,Tang X O,Lu H Q,Ma S D.Face recognition using kernel scatter-difference-based discriminant analysis.Neural Networks,IEEE Transactions on,2006,17(4):1081-1085.
[56]Francis R B;Michael I J.Kernel Independent Component Analysis.Journal of Machine Learning Research,2002,(3):1-48.
[57]黄国宏,邵惠鹤.核主元分析及其在人脸识别中的应用.计算机工程,2004,30(13):13-14.
[58]陈绵书,陈贺新,刘伟.一种新的求取无相关鉴别矢量集算法.计算机学报,2004,27(7):913-917.
[59]黄修武,杨静宇,郭跃飞.基于隶属度的人脸图像特征提取与识别[J].电子学报,1998,26(5):89-92.
[60]陈武凡.小波分析及其在图像处理中的应用.北京:科学出版社,2002.
[61]Manjunath B S,Chellappa R.A feature based approach to face recognition.IEEE Proc.CVPR.1992:373-378
[62]Nastar C,Ayache N.Frequency-based nonrigid motion analysis:application to four dimensional medical images.IEEE Trans.PAMI,1996,18(11):1067-1079.
[63]赖剑煌,阮邦志,冯国灿.频谱脸.一种基于小波变换和Fourier变换的人脸识别新方法.中国图像图形学报,1999,4(10):811-817.
[64]Lai J H,Yuen P C,Feng G C.Face recognition using holistic Fourier invariant features.Pattern Recognition,2001,34(1):95-109
[65]Sergios T,Konstantinos K,模式识别(第二版).李晶皎,朱志良,王爱侠等译.北京:电子工业出版社,2004.
[66]Hafed Z M,Levine M D.Face Recognition Using The Discrete Cosine Transform[J].International J of Computer Vision,2001,43(3):167-188.
[67]Daugman J G.Uncertainty Relation for Resolution in Space,Spatial Frequency,and Orientation Optimized by Two-Dimensional Visual Cortical Filters.J.Optical Soc.Amer,1985,2(7):160-169.
[68]Lee T S.Image Representation Using 2d Gabor Wavelets.IEEE Trans.Pattern Analysis and Machine Intelligence,1996,18(10):959-971.
[69]Donato G,Bartlett M S,Hager J C,Ekman P,Sejnowski T J,Classifying facial actions.IEEE Trans.Pattern Anal.Machine Intell,vol.1999,21:974-989.
[70]Liu C J,Wechsler H,Independent Component Analysis of Gabor Features for Face Recognition.IEEE Tran.on Neural Networks,2003,14(4):919-928.
[71]Shen L L,Bai L,Fairhurst M,Gabor wavelets and General Discriminant Analysis for face identification and verification.Image and Vision Computing,2007,25:553 -563.
[72]Bouattour H,Vienner E.Solving the Human face recognition task using neural nets.Proceeding Artificial Neural Networks,Amsterdam North-Holland,1992,1595-1598.
[73]Kohonen T.Associative Memory:A System Theoretic Approach.Berlin:Springer,1997.
[74]Lee S Y,Ham Y K,Park R H.Recognition of human front faces using knowledge-based feature extraction and neuro-fuzzy algorithm.Pattern Recognition,1996,29(11):1863-1879.
[75]Lawrence S,Giles C L,Tsoi A C,Back A D.Face recognition:A convolutional neural-net+ork approach.IEEE Trans.Neural Networks,1997,8(1):98-113
[76]Nello C,John S T.支持向量机导论.李国正,王猛,曾华军等译,北京:电子工业出版社,2004
[77]Mirhosseini A R,YaH H,Lam K M,Pham T.Human face image recognition:an evidence aggregation approach.Computer Vision and Image Understanding,1998,71(2):213-220.
[78]Meng J E,Shi Q W,Ju W L,Hock L T.Face Recognition With Radial Basis Function (RBF)Neural Networks.IEEE TRANSACTIONS ON NEURAL NETWORKS,2002,13(3):697-710.
[79]甘俊英,张有为.一种基于奇异值特征的神经网络人脸识别新途径.电子学报,2004 1:170-173.
[80]Vapnik K.An overview of statistical learning theory.IEEE Trans,Neural Network,1999,10:988-999,.
[81]Cortes C,Vapnik V.Support vector networks.Mach Learn,1995,20:273-297.
[82]Osuna F E,Girosi F.Training Support Vector Machines:An Application to Face Detection,IEEE Conference on Computer Vision and Pattern Recognition,1997,130-136.
[83]Guo G D,Li S Z,Chan K L.Face Recognition by Support Vector Machines.Proceedings of Fourth IEEE International Conference on Automastic Face and Gesture Recognition,2000,196-201.
[84]Xi D,Podolak I,Lee S.Facial component extraction end face recognition with support vector machines.In Proc.IEEE Intl.Conf.Auto.Face&Gesture Recog,2002,83-88.
[85]王宏漫,欧宗瑛.采用PCA/ICA特征和SVM分类的人脸识别[J].计算机辅助设计及图形学学报,2003,15(4):416-420.
[86]陶亮,庄镇泉.一种基于个人身份认证的正面人脸识别算法.中国图象图形学报,2003(8).
[87]Li Z F,Tang X O.Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition.Information Forensics and Security,IEEE Transactions on,2007,2(2):174-180.
[88]Liu Y H,Chen Y T.Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines.Neural Networks,IEEE Transactions on,2007,18(1):178-192.
[89]Luo B,Zhang Y,Pan Y H.Face recognition based on wavelet transform and SVM.Information Acquisition,2005 IEEE International Conference on,2005,27
[90]张燕昆,杜平,刘重庆.基于主元分析与支持向量机的人脸识别方法[J].上海交通大学学报,2002,36(6):884-886.
[91]凌旭峰,扬杰,叶晨洲.基于支持向量机的人脸识别技术.2001,30(5):5-10.
[92]孙大瑞,吴乐南.基于非线性特征提取和SVM的人脸识别算法,电子与信息学报,200426(1):307-311.
[93]崔国勤,高文.墓于双层虚拟视图和支持向量机的人脸识别方法.计算机学报,2005,28(3):368-376.
[94]边肇祺,张长水,张学工.模式识别[M].北京:清华大学出版社,2000.
[95]Moghaddam B,Jebara T,Pentland A.Bayesian Face Recognition,Pattern Recognition,2000,33:1771-1782.
[96]Schneiderman H,Kanade K.A statistical method for 3D object detection applied to faces and cars.In Proceeding CVPR 2000,2000,746- 751.
[97]Moghaddatn B,Pentland A.Probabilistic visual learning for object representation.IEEE Trans.PAMI,1997,19(7):696-710.
[98]Samaria F.Face Recognition using hidden Markov models.Ph.D.thesis,University of Cambridge,1994.
[99]Bicego M,Castellani U,Murino V.Using Hidden Markov Models and Wavelets for Face Recognition.Proceedings of the 12th International Conference on Image Analysis and Processing,2003.
[100]Kohir V V,Desai U B.Face recognition using DCT HMM approach.In Workshop on Advances in Facial Image Analysis and Recognition Technology(AFIART),Freiburg,Germany,June 1998.
[101]Liu X J,Wang D F,Zhang L F,Shi Y G,Zou M Y.An Approach for Face Recognition Based on Singular Value Decomposition and Hidden Markov Model.Chinese journal of computers,2003,26(3):340-344.
[102]薛斌党,欧宗瑛.加权合成的嵌入式隐Markov模型人脸识别.大连理工大学学报,2002,42(3):326-332.(102)
[103]http://www.cl.cam.ac.uk/Research/DTG/attarchive/facedatabase.html.
[104]http://cvc.yale.edu/projects/yalefaces/yalefaces.html
[105]http://cobweb.ecn.purdue.edu/~aleix/aleix_face_DB.html
[106]http://www.anefian.com/face_reco.htm
[107]http://www.itl.nist.gov/iad/humanid/colorferet/home.html.
[108]Gross R,Li S Z,Jain A K.Face Databases,Handbook of Face Recognition.Springer,2005.
[109]Strang G.Linear Algebra and Its Applications(2nd edition),Academic Press,1980.
[110]Cardoso J F.Blind Signal Separation Statistical Principle.Proceedings of the IEEE.special issue on blind identification and estimation,1998,9(10):2009-2025.
[111]Bell A J,Sejnowski T J.An information-maxization approach to blind separation and blind deconvolution.Neural computation,1995,7(6):1129-1159.
[112]Xu Y Q,Li B C,Wang B.Face recognition by fast independent component analysis and genetic algorithm.Proceedings of the Fourth International Conference on Computer and Information Technology(CIT' 04),2004.
[113]Wang Y,Jia Y D,Hu C B,et al.Fisher non-negative matrix factorization for learning local features.Asian Conference on Computer Vision,Korea,2004.
[114]陈卫刚,戚飞虎.可行方向算法与模拟退火结合的NMF特征提取方法.电子学报,2003年12月,第12A期31卷,2190-2193.
[115]Zhou W,Pu X R.Parts-Based Holistic Face Recognition with RBF Neural Networks.ISNN(2)2006,110-115.
[116]胡良梅.基于D-S证据理论的神经网络形状识别系统.合肥工业大学硕士学位论文,2002.
[117]何友,王国宏,陆大金,彭应宁.多传感器信息融合及应用.北京:电子工业出版社2000.
[118]Pau L F.Fusion of Multisensor Data in Pattern Recognition in Pattern Recognition Theory and Application.Reidel,1982:189-201.
[119]孔锐,施泽生,郭立,张国宣.基于全局特征和局部特征组合的脸谱识别方法.系统仿真学报,2004(5).
[120]王蕴红等.融合全局与局部特征的子空间人脸识别算法.计算机学报,2005,28(10):1657-1663.
[121]耿伯英,彭稳高,朱强华,杨静宇.基于信息融合的模式识别方法研究.模式识别与人工智能,2002,15(3):339-342.
[122]陈文颉,窦丽华,陈杰,杜丽辉.融合多特征信息的模式识别方法.北京理工大学学报,2002,22(2):173-176.
[123]刘瑾,徐可欣,陈小红,吴萍,赵学玲.采用图像融合技术的多模式人脸识别.工程图学学报,2007(6).
[124]Ahmed,Natarejan N T,Rap K.Discrete Cosine Transform[J].IEEE Trans.On Computers,1974,23(1):90-93.
[125]Liu C J.Gabor-based Kernel PCA with fractional power polynomial models for face recognition.IEEE Transactions on PAMI,2005,26(5).
[126]Wang L,Li Y P.A novel 2D Gabor wavelets window method for face recognition.Lecture Notes in Computer Science,2006,41(5):497- 504.
[127]赵力.语音信号处理[M].北京:机械工业出版社,2003.
[128]谢锦辉.HMM及其在语音处理中的应用[M].武汉:华中理工大学出版社,1995.
[129]Burges C J C.A Tutorial on Support Vector Machines for Pattern Recognition.Knowledge Discovery and Data Mining,1998,2(2):121-167
[130]Vapnik V.Statistical Learning Theory.Wiley,1998.
[131]杨淑莹.VC++图像处理程序设计(第2版)[M].北京:清华大学出版社,2005.
[132]Liu C J,Wechsler H,A Gabor feature classifier for face recognition.Computer Vision,2001,2:270 -275.
[133]程正兴,杨守志,冯晓霞,小波分析的理论算法进展和应用.北京:国防工业出版社,2007
[134]苏剑波,徐波编著,应用模式识别技术导论:人脸识别与语音识别.上海:上海交通大学出版社,2001。
[135]李道本,信号的统计检测与估计理论.北京:北京邮电大学,1996.
[136]韩崇昭,朱洪艳,段战胜等著,多源信息融合.北京:清华大学出版社,2006.
[137]胡广书编著.数字信号处理.北京:清华大学出版社,1997.
[138]李云峰,欧宗瑛.基于Gabor小波变换和支持向量机的人脸识别.计算机工程,2006,32(19):181-182.
[139]Philllips P J,Moon H,Rizvi S A,et al.The FERET Evaluation Methodology for Face Recognition Alogrithms.IEEE Transaction on Pattern Analysis and Machine Intelligence,2000,22(10):1090-1104.
[140]Blackburn D M,Bone M,Phillips P J.Face Recognition Vendor Test 2000.Evaluation Report,2001.
[141]Phillips P J,Grother P,Micheals R J,et al.Face Recognition Test 2002:Overview and Summary.Gaithersburg,MD:National Institute of Standards and Technology,2003.
[142]Phillips P J,Scruggs W T,Toole A J,et al.FRVT 2006 and ICE2006 Large-Scale Results,2007.
[143]Li S Z,Jain A K.Handbook of Face Recognition.Springer-verlag,New York,2005.
[144]Adini Y,Moses Y,Ullman S.Face Recognition:The Problem of Compensating for changes in illumination Direction.IEEE Transaction on Pattern Analysis and Machine Intelligence,1997,19(7):721-732.
[145]Rabiner L.A tutorial on Hidden Markov Models and selected application in speech recognition.Proceedings of IEEE,1989,77:256-286.
[146]Ding X Q.Information Entropy Theory in Pattern Recognition.Acta Electronica Sinica,1992,21(8):2-8.
[147]Zhao W,Chellappa R,Rosenfeld A,Phillips P J.Face recognition:A literature survey.Computer Vision Laboratory,University of Maryland,Technical Reports CAR2TR2948,2000
[2]包桂秋,林喜荣,苏晓生,周兆英.基于人体生物特征的身份鉴别技术发展概况.清华大学学报(自然科学版),2001,41(4):72-76.
[3]山世光.人脸识别中若干关键问题的研究:(博士学位论文).北京中国科学院,2004.
[4]http://www.biometricgroup.com/reports/public/market_report.html
[5]Chellappa R et al.Human and Machine Recognition of Faces:A Survey[J].Proceedings of the IEEE,1995,83(5):705-740.
[6]周杰,卢春雨,张长水,李衍达.人脸自动识别方法综述.电子学报,2000,5(4):102-106.
[7]张翠平,苏光大.人脸识别技术综述.中国图像图形学报,2001,5(11):885-894。
[8]周激流,张晔.人脸识别理论研究进展[J].计算机辅助设计与图形学学报,1999,11(2):180-184.
[9]肖兵,王英辉.人脸识别研究综述.陕西师范大学学报2005.8:1-5.
[10]Andrea F A,Michele N,Daniel R and Gabriele S,2D and 3D face recognition:A survey,Pattern Recognition Letters,2007,28(14):1885-1906
[11]段锦,周春光,刘小华.三维人脸识别研究进展.小型微型计算机系统,2004,25(5):886-890.
[12]刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法.自动化学报,2003,29(6):900-911.
[13]Bledsoe W W.Man Machine Facial Recognition[R].Palo Alto CA,1966.
[14]Kelly M D.Visual identification of people by computer.Tech.Rep.AI-130,Stanford AI Project,Stanford,CA,1970.
[15]Brunelli R,Poggio T.Feature recognition:Feature versus templates.IEEE Transactions on PAMI,1993,15(10):1024-1052.
[16]Roeder N,Li X B.Accuracy analysis for facial feature detection.Pattern Recognition,1996,29(1):143-157.
[17]Laurenz W,Jean M F,Norbert K,et al.Face Recognition by Elastic Graph Matching [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):775-779.
[18]Lades M,Vorbuggen J,Buhmann J.Distortion invariant object recognition in the dynamic link architecture.IEEE Trans.Computers,1993,42(3):300-311.
[19]Wiskott L,Fellous J M,Kruger N,Malsburg C V.Face recognition by elastic bunch graph matching.IEEE Trans.PAMI,1997,19(7):775-779.
[20]Lee M W,Ranganath S.Pose-invariant face recognition using a 3D deformable model.Pattern Recognition,2003,36(8):1835-1846.
[21]薛理立,艾海舟.基于弹性图匹配的实时视频流人脸识别.计算机科学,2003(11).
[22]尹宝才,张壮,孙艳丰,王成章.基于三维形变模型的多姿态人脸识别.北京工业大学学报,2007(3).
[23]山世光,高文等.基于纹理分布和变形模板的面部特征提取.软件学报,2001,12(4):570-577.
[24]Kirby M,Sirovich L.Application of the KL procedure for the characterization of human faces,IEEE Trans.Pattern and Machine Intelligence,1990,12(1):103-108
[25]Turk M A,Pentland A P.Eigenfaces for recognition.Journal of Cognitive Neuroscience,1991,3(1):71-86
[26]Zhang J,Yan Y,Lades M.Face recognition:Eigenfaces,elastic Matching,and neural nets.IEEE Proc,1997,85(9):1423-1435.
[27]Lanitis A,Taylor C J,Cootes T F.Automatic interpretation and coding of face images using flexible models.IEEE Trans.PAMI,1997,19(7):743-756.
[28]Pentland A,Moghaddatn B,Starner T.View-based and modular eigenspaces for face recognition.IEEE Proc.CVPR,1994:84-91.
[29]Liu C J,Wechsler H.Probabilistic reasoning model for face recognition.IEEE Proc.CVPR,1998:827-832.
[30]羊牧,周激流,胡艳梅.基SVD和KL投影相融合的人脸识别算法.成都信息工程学院学报,2007,22(5):602-605.
[31]Moghaddatn B,Jebara T,Pentland A.Bayesian face recognition.Pattern Recognition,2000,33(11):1771-1782
[32]杨琼,丁晓青.对称主分量分析及其在人脸识别中的应用.计算机学报,2003.26:9-15.
[33]彭辉,张长水,荣钢,等.基于K-L变换的人脸识别自动识别方法[J],清华大学学报(自然科学版),1997,37(3):67-70.
[34]陈刚,戚飞虎.实用人脸识别系统的本征脸法实现.计算机研究与发展,2001(2)
[35]高全学,潘泉,梁彦,张洪才,程咏梅.基于描述特征的人脸识别研究.自动化学报,2006(3).
[36]Klema V C.The singular value decomposition:Its computation and some applications.IEEE Transaction son Automatic Control,1980,25:164- 176.
[37]Hong Z Q.Algebraic feature extraction of image recognition.Recognition.1991,24(3):211-219.
[38]Cheng Y Q,Yang J Y.A Novel Feature Extraction Method for Image Recognition Based On Similar Oiscriminant Function.Pattern Recognition,1993,26(1):115-125.
[39]Zhao H,Yuen P C,Incremental Linear Discriminant Analysis for Face Recognition,IEEE Transactions on.Systems,Man,and Cybernetics,Part B.2008,38(1):210-221.
[40]Zuo W M,Zhang D,Yang J,Wang K Q.BDPCA plus LDA:a novel fast feature extraction technique for face recognition,IEEE Transactions on.Systems,Man,and Cybernetics,Part B.2006,36(4):946-953.
[41]Xiang C,Fan X A,Lee T H.Face recognition using recursive Fisher linear discriminant,Image Processing,IEEE Transactions on.2006,15(8):2097-2105.
[42]Zheng W S,Lai J H,Yuen P C.GA-fisher:a new LDA-based face recognition algorithm with selection of principal components.IEEE Transactions on.Systems,Man,and Cybernetics,Part B.2005,35(5):1065- 1078.
[43]Liu Q S,Lu H Q,Ma S D.Improving kernel Fisher discriminant analysis for face recognition.Circuits and Systems for Video Technology,IEEE Transactions on,2004,14(1):42-49.
[44]Common P.Independent component analysis:A new concept.Signal Processing,1994,36(3):287-314.
[45]Bartlett M S,Lades H M,Sejnowski T J.Independent component representations for face recognition.SPIE Proc.1998,5,28-64.
[46]Kim T K,Kim H,Hwang W,Kittler J.Independent component analysis in a local residue space for face recognition.Pattern Recognition,2004,37(9):1873-1885.
[47]Deniz O,Castrillon M,Hernandez M.Face recognition using independent component analysis and support vector machines.Pattern Recognition Letters,2003,24(13):2153-2157.
[48]Liu C J,Wechsler H.Comparative assessment of independent component analysis(ICA)for face recognition.In:Proceedings of International Conference on Audio and Video Based Biometric Person Authentication,USA,Washington DC,1999.
[49]杨竹青,李勇,胡德文.独立成分分析方法综述[J].自动化学报,2002,28(5):762-772.
[50]Hyvarinen A.Fast and robust fixed-point algorithms for independent component analysis,IEEE Trans.Neural Networks,1999,10:626-634.
[51]Lee D D,Seung H S.Learning the parts of objects by non-negative matrix factorization [J].Nature,1999,401(10):788-791.
[52]David G,Jordi V.Non-negative Matrix Factorization for Face Recognition.Lecture Notes in Computer Science,2002,25(4):336-344.
[53]Li S Z,Hou X W,Zhang H J,et al.Learning Spatially Localized,Part-based Representation.IEEE Conf.on Computer Vision and Pattern Recognition,2001,1:207-212.
[54]Feng T,Li S Z,Shum H,et al.Local Non-Negative Matrix Factorization as a Visual Representation.Proceedings of The 2nd International Conference on Development and Learning,2002.
[55]Liu Q S,Tang X O,Lu H Q,Ma S D.Face recognition using kernel scatter-difference-based discriminant analysis.Neural Networks,IEEE Transactions on,2006,17(4):1081-1085.
[56]Francis R B;Michael I J.Kernel Independent Component Analysis.Journal of Machine Learning Research,2002,(3):1-48.
[57]黄国宏,邵惠鹤.核主元分析及其在人脸识别中的应用.计算机工程,2004,30(13):13-14.
[58]陈绵书,陈贺新,刘伟.一种新的求取无相关鉴别矢量集算法.计算机学报,2004,27(7):913-917.
[59]黄修武,杨静宇,郭跃飞.基于隶属度的人脸图像特征提取与识别[J].电子学报,1998,26(5):89-92.
[60]陈武凡.小波分析及其在图像处理中的应用.北京:科学出版社,2002.
[61]Manjunath B S,Chellappa R.A feature based approach to face recognition.IEEE Proc.CVPR.1992:373-378
[62]Nastar C,Ayache N.Frequency-based nonrigid motion analysis:application to four dimensional medical images.IEEE Trans.PAMI,1996,18(11):1067-1079.
[63]赖剑煌,阮邦志,冯国灿.频谱脸.一种基于小波变换和Fourier变换的人脸识别新方法.中国图像图形学报,1999,4(10):811-817.
[64]Lai J H,Yuen P C,Feng G C.Face recognition using holistic Fourier invariant features.Pattern Recognition,2001,34(1):95-109
[65]Sergios T,Konstantinos K,模式识别(第二版).李晶皎,朱志良,王爱侠等译.北京:电子工业出版社,2004.
[66]Hafed Z M,Levine M D.Face Recognition Using The Discrete Cosine Transform[J].International J of Computer Vision,2001,43(3):167-188.
[67]Daugman J G.Uncertainty Relation for Resolution in Space,Spatial Frequency,and Orientation Optimized by Two-Dimensional Visual Cortical Filters.J.Optical Soc.Amer,1985,2(7):160-169.
[68]Lee T S.Image Representation Using 2d Gabor Wavelets.IEEE Trans.Pattern Analysis and Machine Intelligence,1996,18(10):959-971.
[69]Donato G,Bartlett M S,Hager J C,Ekman P,Sejnowski T J,Classifying facial actions.IEEE Trans.Pattern Anal.Machine Intell,vol.1999,21:974-989.
[70]Liu C J,Wechsler H,Independent Component Analysis of Gabor Features for Face Recognition.IEEE Tran.on Neural Networks,2003,14(4):919-928.
[71]Shen L L,Bai L,Fairhurst M,Gabor wavelets and General Discriminant Analysis for face identification and verification.Image and Vision Computing,2007,25:553 -563.
[72]Bouattour H,Vienner E.Solving the Human face recognition task using neural nets.Proceeding Artificial Neural Networks,Amsterdam North-Holland,1992,1595-1598.
[73]Kohonen T.Associative Memory:A System Theoretic Approach.Berlin:Springer,1997.
[74]Lee S Y,Ham Y K,Park R H.Recognition of human front faces using knowledge-based feature extraction and neuro-fuzzy algorithm.Pattern Recognition,1996,29(11):1863-1879.
[75]Lawrence S,Giles C L,Tsoi A C,Back A D.Face recognition:A convolutional neural-net+ork approach.IEEE Trans.Neural Networks,1997,8(1):98-113
[76]Nello C,John S T.支持向量机导论.李国正,王猛,曾华军等译,北京:电子工业出版社,2004
[77]Mirhosseini A R,YaH H,Lam K M,Pham T.Human face image recognition:an evidence aggregation approach.Computer Vision and Image Understanding,1998,71(2):213-220.
[78]Meng J E,Shi Q W,Ju W L,Hock L T.Face Recognition With Radial Basis Function (RBF)Neural Networks.IEEE TRANSACTIONS ON NEURAL NETWORKS,2002,13(3):697-710.
[79]甘俊英,张有为.一种基于奇异值特征的神经网络人脸识别新途径.电子学报,2004 1:170-173.
[80]Vapnik K.An overview of statistical learning theory.IEEE Trans,Neural Network,1999,10:988-999,.
[81]Cortes C,Vapnik V.Support vector networks.Mach Learn,1995,20:273-297.
[82]Osuna F E,Girosi F.Training Support Vector Machines:An Application to Face Detection,IEEE Conference on Computer Vision and Pattern Recognition,1997,130-136.
[83]Guo G D,Li S Z,Chan K L.Face Recognition by Support Vector Machines.Proceedings of Fourth IEEE International Conference on Automastic Face and Gesture Recognition,2000,196-201.
[84]Xi D,Podolak I,Lee S.Facial component extraction end face recognition with support vector machines.In Proc.IEEE Intl.Conf.Auto.Face&Gesture Recog,2002,83-88.
[85]王宏漫,欧宗瑛.采用PCA/ICA特征和SVM分类的人脸识别[J].计算机辅助设计及图形学学报,2003,15(4):416-420.
[86]陶亮,庄镇泉.一种基于个人身份认证的正面人脸识别算法.中国图象图形学报,2003(8).
[87]Li Z F,Tang X O.Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition.Information Forensics and Security,IEEE Transactions on,2007,2(2):174-180.
[88]Liu Y H,Chen Y T.Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines.Neural Networks,IEEE Transactions on,2007,18(1):178-192.
[89]Luo B,Zhang Y,Pan Y H.Face recognition based on wavelet transform and SVM.Information Acquisition,2005 IEEE International Conference on,2005,27
[90]张燕昆,杜平,刘重庆.基于主元分析与支持向量机的人脸识别方法[J].上海交通大学学报,2002,36(6):884-886.
[91]凌旭峰,扬杰,叶晨洲.基于支持向量机的人脸识别技术.2001,30(5):5-10.
[92]孙大瑞,吴乐南.基于非线性特征提取和SVM的人脸识别算法,电子与信息学报,200426(1):307-311.
[93]崔国勤,高文.墓于双层虚拟视图和支持向量机的人脸识别方法.计算机学报,2005,28(3):368-376.
[94]边肇祺,张长水,张学工.模式识别[M].北京:清华大学出版社,2000.
[95]Moghaddam B,Jebara T,Pentland A.Bayesian Face Recognition,Pattern Recognition,2000,33:1771-1782.
[96]Schneiderman H,Kanade K.A statistical method for 3D object detection applied to faces and cars.In Proceeding CVPR 2000,2000,746- 751.
[97]Moghaddatn B,Pentland A.Probabilistic visual learning for object representation.IEEE Trans.PAMI,1997,19(7):696-710.
[98]Samaria F.Face Recognition using hidden Markov models.Ph.D.thesis,University of Cambridge,1994.
[99]Bicego M,Castellani U,Murino V.Using Hidden Markov Models and Wavelets for Face Recognition.Proceedings of the 12th International Conference on Image Analysis and Processing,2003.
[100]Kohir V V,Desai U B.Face recognition using DCT HMM approach.In Workshop on Advances in Facial Image Analysis and Recognition Technology(AFIART),Freiburg,Germany,June 1998.
[101]Liu X J,Wang D F,Zhang L F,Shi Y G,Zou M Y.An Approach for Face Recognition Based on Singular Value Decomposition and Hidden Markov Model.Chinese journal of computers,2003,26(3):340-344.
[102]薛斌党,欧宗瑛.加权合成的嵌入式隐Markov模型人脸识别.大连理工大学学报,2002,42(3):326-332.(102)
[103]http://www.cl.cam.ac.uk/Research/DTG/attarchive/facedatabase.html.
[104]http://cvc.yale.edu/projects/yalefaces/yalefaces.html
[105]http://cobweb.ecn.purdue.edu/~aleix/aleix_face_DB.html
[106]http://www.anefian.com/face_reco.htm
[107]http://www.itl.nist.gov/iad/humanid/colorferet/home.html.
[108]Gross R,Li S Z,Jain A K.Face Databases,Handbook of Face Recognition.Springer,2005.
[109]Strang G.Linear Algebra and Its Applications(2nd edition),Academic Press,1980.
[110]Cardoso J F.Blind Signal Separation Statistical Principle.Proceedings of the IEEE.special issue on blind identification and estimation,1998,9(10):2009-2025.
[111]Bell A J,Sejnowski T J.An information-maxization approach to blind separation and blind deconvolution.Neural computation,1995,7(6):1129-1159.
[112]Xu Y Q,Li B C,Wang B.Face recognition by fast independent component analysis and genetic algorithm.Proceedings of the Fourth International Conference on Computer and Information Technology(CIT' 04),2004.
[113]Wang Y,Jia Y D,Hu C B,et al.Fisher non-negative matrix factorization for learning local features.Asian Conference on Computer Vision,Korea,2004.
[114]陈卫刚,戚飞虎.可行方向算法与模拟退火结合的NMF特征提取方法.电子学报,2003年12月,第12A期31卷,2190-2193.
[115]Zhou W,Pu X R.Parts-Based Holistic Face Recognition with RBF Neural Networks.ISNN(2)2006,110-115.
[116]胡良梅.基于D-S证据理论的神经网络形状识别系统.合肥工业大学硕士学位论文,2002.
[117]何友,王国宏,陆大金,彭应宁.多传感器信息融合及应用.北京:电子工业出版社2000.
[118]Pau L F.Fusion of Multisensor Data in Pattern Recognition in Pattern Recognition Theory and Application.Reidel,1982:189-201.
[119]孔锐,施泽生,郭立,张国宣.基于全局特征和局部特征组合的脸谱识别方法.系统仿真学报,2004(5).
[120]王蕴红等.融合全局与局部特征的子空间人脸识别算法.计算机学报,2005,28(10):1657-1663.
[121]耿伯英,彭稳高,朱强华,杨静宇.基于信息融合的模式识别方法研究.模式识别与人工智能,2002,15(3):339-342.
[122]陈文颉,窦丽华,陈杰,杜丽辉.融合多特征信息的模式识别方法.北京理工大学学报,2002,22(2):173-176.
[123]刘瑾,徐可欣,陈小红,吴萍,赵学玲.采用图像融合技术的多模式人脸识别.工程图学学报,2007(6).
[124]Ahmed,Natarejan N T,Rap K.Discrete Cosine Transform[J].IEEE Trans.On Computers,1974,23(1):90-93.
[125]Liu C J.Gabor-based Kernel PCA with fractional power polynomial models for face recognition.IEEE Transactions on PAMI,2005,26(5).
[126]Wang L,Li Y P.A novel 2D Gabor wavelets window method for face recognition.Lecture Notes in Computer Science,2006,41(5):497- 504.
[127]赵力.语音信号处理[M].北京:机械工业出版社,2003.
[128]谢锦辉.HMM及其在语音处理中的应用[M].武汉:华中理工大学出版社,1995.
[129]Burges C J C.A Tutorial on Support Vector Machines for Pattern Recognition.Knowledge Discovery and Data Mining,1998,2(2):121-167
[130]Vapnik V.Statistical Learning Theory.Wiley,1998.
[131]杨淑莹.VC++图像处理程序设计(第2版)[M].北京:清华大学出版社,2005.
[132]Liu C J,Wechsler H,A Gabor feature classifier for face recognition.Computer Vision,2001,2:270 -275.
[133]程正兴,杨守志,冯晓霞,小波分析的理论算法进展和应用.北京:国防工业出版社,2007
[134]苏剑波,徐波编著,应用模式识别技术导论:人脸识别与语音识别.上海:上海交通大学出版社,2001。
[135]李道本,信号的统计检测与估计理论.北京:北京邮电大学,1996.
[136]韩崇昭,朱洪艳,段战胜等著,多源信息融合.北京:清华大学出版社,2006.
[137]胡广书编著.数字信号处理.北京:清华大学出版社,1997.
[138]李云峰,欧宗瑛.基于Gabor小波变换和支持向量机的人脸识别.计算机工程,2006,32(19):181-182.
[139]Philllips P J,Moon H,Rizvi S A,et al.The FERET Evaluation Methodology for Face Recognition Alogrithms.IEEE Transaction on Pattern Analysis and Machine Intelligence,2000,22(10):1090-1104.
[140]Blackburn D M,Bone M,Phillips P J.Face Recognition Vendor Test 2000.Evaluation Report,2001.
[141]Phillips P J,Grother P,Micheals R J,et al.Face Recognition Test 2002:Overview and Summary.Gaithersburg,MD:National Institute of Standards and Technology,2003.
[142]Phillips P J,Scruggs W T,Toole A J,et al.FRVT 2006 and ICE2006 Large-Scale Results,2007.
[143]Li S Z,Jain A K.Handbook of Face Recognition.Springer-verlag,New York,2005.
[144]Adini Y,Moses Y,Ullman S.Face Recognition:The Problem of Compensating for changes in illumination Direction.IEEE Transaction on Pattern Analysis and Machine Intelligence,1997,19(7):721-732.
[145]Rabiner L.A tutorial on Hidden Markov Models and selected application in speech recognition.Proceedings of IEEE,1989,77:256-286.
[146]Ding X Q.Information Entropy Theory in Pattern Recognition.Acta Electronica Sinica,1992,21(8):2-8.
[147]Zhao W,Chellappa R,Rosenfeld A,Phillips P J.Face recognition:A literature survey.Computer Vision Laboratory,University of Maryland,Technical Reports CAR2TR2948,2000
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |