基于学习的人脸识别研究
|
摘要
人脸识别是生物特征识别的关键技术之一,其最核心的两大步骤为人脸检测与识别。它的主要任务就是从图像或视频中准确地找出人脸并确定其身份。本文从基于学习的角度出发,在聚类、流形、子空间学习等机器学习方法的基础上,提出了改进或新的人脸识别方法,并和其他人脸识别方法作比较,取得了较好的效果。本文主要研究了以下几方面的内容:
1)针对人脸检测问题,通过肤色法分离人脸目标与背景后,提出两种方法定位人脸即:积分投影—高斯曲线法和改进减法聚类法,分别对应单人脸和多人脸检测。积分投影—高斯曲线法将人脸二值图像分别在X、Y轴积分投影,根据投影曲线分别计算相应的高斯曲线,通过求解高斯方程快速得到较为准确的人脸区域。改进减法聚类法运用一种新的距离定义,通过图像中人脸目标的统计信息对算法的关键参数进行预估计,能自动结束人脸目标搜索。下采样降低减法聚类的运算量,提高了算法的运行速度,同时验证了减法聚类在视频运动目标定位中的良好应用效果。精确地检测人脸需要对不同姿态的人脸进行姿态角估计,本文在肤色区域提取的基础上,提出姿态角度估计目标函数,并讨论了两种寻优方法,即梯度下降法和次全局枚举寻优法,来估计姿态角度值。根据估计的姿态角度作相应的旋转校正,在校正后的区域利用眼睛和嘴的色度和亮度特点分别构造映射图,提取出眼睛和嘴,并验证人脸。
2)针对流形学习的人脸识别问题,围绕流形学习方法的本质要素,即:(1)如何构造近邻结构图;(2)以什么样的距离测度来衡量人脸样本的近邻;(3)遵循什么样的目标准则来构造低维嵌入,从三方面入手,衍生了中心近邻嵌入学习和鉴别矢量角嵌入学习两种新的流形学习方法。中心近邻嵌入的学习算法,与经典的局部线性嵌入和保局映射不同,它是一种有监督的线性降维方法。该方法首先通过计算各类样本中心,并引入中心近邻距离代替两样本点之间的直接距离作为权系数函数的输入;然后在保持中心近邻几何结构不变的情况下把高维数据嵌入到低维坐标系中。鉴别矢量角嵌入的识别方法,构造了一幅有正/负连接边的邻接图,算法中连接边权系数的测度采用矢量角代替矢量模,不但省去了传统方法中对热核权函数t参数的估计,而且降低由于图像样本间的亮度差异对识别率造成的影响。
3)为了实现人脸识别免于特征提取,提出了一种基于正交补脸的人脸识别方法。该方法基于空间正交分解理论,首先对不同类的原始训练样本进行Gram-Schmidt正交化,以正交化后的基张成各个不同的子空间,然后把测试样本分解为子空间投影及子空间正交补两部分。正交补的范数体现了测试样本到各类子空间的距离,并以此作为分类的依据。
4)针对单样本人脸识别问题,本文提出了一种基于单样本切割的子模块主成分分析方法。该方法将单样本人脸图片切割成大小相同、互不重叠的多个子模块,构成新的样本集。对所有子模块作主成分分析(PCA)并提取特征,同一人脸的子模块特征系数作为分类识别的依据。
Face recognition is a key technique among biometric identification technologies, and its most important components are face detection and recognition. The aim of face recognition is to detect faces from images or videos accurately and recognize their identities. This dissertation focuses on learning-based face recognition, including machine learning methods such as clustering, manifold and subspace learning. The contributions of the dissertation are:
1) To deal with the problem of face detection, two methods are proposed based on skin detection, which are called integral projection—Gaussian curves and modified substractive clustering, corresponding to single face and muti-face detection. In the approach of integral projection—Gaussian curves, two curves are obtained by integral projecting the binary-image to X and Y axes respectively, from which Gaussian curves are calculated and then, an accurate face region is found rapidly through the solution of Gaussian equation. The modified clustering algorithm proposes a new definition of distance for multi-face detection, and its key parameters can be predetermined adaptively by statistical information of face objects in the image. Downsampling is employed to reduce the computation of clustering and speed up the process of the proposed method. Meanwhile, the proposed approach also implements well in location of moving objects in video sequence. In order to estimate the angle of pose accurately, a cost function is proposed. The methods of gradient descent and sub-global enumerating are employed to search for the angle of pose. By rotating the image with the estimated angle, the pose is calibrated. And then, the eye map and mouth map are constructed by their characteristics of chroma and lum in the candidate region. Consequently, eyes and mouth are extracted for face validation.
2) Focusing on the 3 essentials of manifold learning in face recognition, namely, (1) how to construct the neighborhood graph; (2) which measure can be used to estimate the true distance between two face samples; (3) what is the suitable cost function for embedding into subspace, two novel learning algorithms are derived from the manifold learning, which is called center based neighborhood embedding(CNE) and discriminant vector angle embedding(DVAE). Unlike the classical methods such as local linear embedding(LLE) and local preserving projection(LPP), CNE is a supervised linear dimensionality reduction method. It first computes centers of all sample classes. The input of the weight function between two samples is replaced by center based neighborhood(CN) distance. Then, the high-dimensional data are embedded into a low-dimensional space with preserving the CN geometric structure. On the other hand, DVAE constructs a graph with both positive and negative edges. The measure in DVAE is the angle between two vectors instead of modulus in traditional methods. It can be exempted from the estimation of the parameter in heat weight function. When test sample is embedded into low-dimensional space, a classification called angle nearest neighbor is used for face recognition.
3) In ordrer to free face recognition from feature extraction, a method called orthogonal complement faces (OC-faces) is presented. The method is based on the orthogonal decomposition theorem. Firstly, the Gram-Schmidt orthogonal transformation is performed on the original training data of each class. Secondly, the orthogonal basis of each class spans a corresponding subspace. Therefore, the query sample can be decomposed into the sum of two components which are the orthogonal projection of query sample onto the corresponding subspace and the orthogonal complement of the subspace, respectively. Furthermore, the norm of the orthogonal complement indicates the distance between the query sample and the subspace of each class, so it can be used for classification.
4) In order to deal with the problem of face recognition with one sample per person, a method called sub-block principle component analysis (PCA) based on partitions of the sample is presented in this disstertation. It first divides the sample into a few sub-blocks which have equal size and are non-overlapping, and then treats all the sub-blocks as a new sample set. Finally, PCA is performed on all the sub-blocks so as to extract features. Classification is done according to the projection coefficients of sub-blocks of a person.
1)针对人脸检测问题,通过肤色法分离人脸目标与背景后,提出两种方法定位人脸即:积分投影—高斯曲线法和改进减法聚类法,分别对应单人脸和多人脸检测。积分投影—高斯曲线法将人脸二值图像分别在X、Y轴积分投影,根据投影曲线分别计算相应的高斯曲线,通过求解高斯方程快速得到较为准确的人脸区域。改进减法聚类法运用一种新的距离定义,通过图像中人脸目标的统计信息对算法的关键参数进行预估计,能自动结束人脸目标搜索。下采样降低减法聚类的运算量,提高了算法的运行速度,同时验证了减法聚类在视频运动目标定位中的良好应用效果。精确地检测人脸需要对不同姿态的人脸进行姿态角估计,本文在肤色区域提取的基础上,提出姿态角度估计目标函数,并讨论了两种寻优方法,即梯度下降法和次全局枚举寻优法,来估计姿态角度值。根据估计的姿态角度作相应的旋转校正,在校正后的区域利用眼睛和嘴的色度和亮度特点分别构造映射图,提取出眼睛和嘴,并验证人脸。
2)针对流形学习的人脸识别问题,围绕流形学习方法的本质要素,即:(1)如何构造近邻结构图;(2)以什么样的距离测度来衡量人脸样本的近邻;(3)遵循什么样的目标准则来构造低维嵌入,从三方面入手,衍生了中心近邻嵌入学习和鉴别矢量角嵌入学习两种新的流形学习方法。中心近邻嵌入的学习算法,与经典的局部线性嵌入和保局映射不同,它是一种有监督的线性降维方法。该方法首先通过计算各类样本中心,并引入中心近邻距离代替两样本点之间的直接距离作为权系数函数的输入;然后在保持中心近邻几何结构不变的情况下把高维数据嵌入到低维坐标系中。鉴别矢量角嵌入的识别方法,构造了一幅有正/负连接边的邻接图,算法中连接边权系数的测度采用矢量角代替矢量模,不但省去了传统方法中对热核权函数t参数的估计,而且降低由于图像样本间的亮度差异对识别率造成的影响。
3)为了实现人脸识别免于特征提取,提出了一种基于正交补脸的人脸识别方法。该方法基于空间正交分解理论,首先对不同类的原始训练样本进行Gram-Schmidt正交化,以正交化后的基张成各个不同的子空间,然后把测试样本分解为子空间投影及子空间正交补两部分。正交补的范数体现了测试样本到各类子空间的距离,并以此作为分类的依据。
4)针对单样本人脸识别问题,本文提出了一种基于单样本切割的子模块主成分分析方法。该方法将单样本人脸图片切割成大小相同、互不重叠的多个子模块,构成新的样本集。对所有子模块作主成分分析(PCA)并提取特征,同一人脸的子模块特征系数作为分类识别的依据。
Face recognition is a key technique among biometric identification technologies, and its most important components are face detection and recognition. The aim of face recognition is to detect faces from images or videos accurately and recognize their identities. This dissertation focuses on learning-based face recognition, including machine learning methods such as clustering, manifold and subspace learning. The contributions of the dissertation are:
1) To deal with the problem of face detection, two methods are proposed based on skin detection, which are called integral projection—Gaussian curves and modified substractive clustering, corresponding to single face and muti-face detection. In the approach of integral projection—Gaussian curves, two curves are obtained by integral projecting the binary-image to X and Y axes respectively, from which Gaussian curves are calculated and then, an accurate face region is found rapidly through the solution of Gaussian equation. The modified clustering algorithm proposes a new definition of distance for multi-face detection, and its key parameters can be predetermined adaptively by statistical information of face objects in the image. Downsampling is employed to reduce the computation of clustering and speed up the process of the proposed method. Meanwhile, the proposed approach also implements well in location of moving objects in video sequence. In order to estimate the angle of pose accurately, a cost function is proposed. The methods of gradient descent and sub-global enumerating are employed to search for the angle of pose. By rotating the image with the estimated angle, the pose is calibrated. And then, the eye map and mouth map are constructed by their characteristics of chroma and lum in the candidate region. Consequently, eyes and mouth are extracted for face validation.
2) Focusing on the 3 essentials of manifold learning in face recognition, namely, (1) how to construct the neighborhood graph; (2) which measure can be used to estimate the true distance between two face samples; (3) what is the suitable cost function for embedding into subspace, two novel learning algorithms are derived from the manifold learning, which is called center based neighborhood embedding(CNE) and discriminant vector angle embedding(DVAE). Unlike the classical methods such as local linear embedding(LLE) and local preserving projection(LPP), CNE is a supervised linear dimensionality reduction method. It first computes centers of all sample classes. The input of the weight function between two samples is replaced by center based neighborhood(CN) distance. Then, the high-dimensional data are embedded into a low-dimensional space with preserving the CN geometric structure. On the other hand, DVAE constructs a graph with both positive and negative edges. The measure in DVAE is the angle between two vectors instead of modulus in traditional methods. It can be exempted from the estimation of the parameter in heat weight function. When test sample is embedded into low-dimensional space, a classification called angle nearest neighbor is used for face recognition.
3) In ordrer to free face recognition from feature extraction, a method called orthogonal complement faces (OC-faces) is presented. The method is based on the orthogonal decomposition theorem. Firstly, the Gram-Schmidt orthogonal transformation is performed on the original training data of each class. Secondly, the orthogonal basis of each class spans a corresponding subspace. Therefore, the query sample can be decomposed into the sum of two components which are the orthogonal projection of query sample onto the corresponding subspace and the orthogonal complement of the subspace, respectively. Furthermore, the norm of the orthogonal complement indicates the distance between the query sample and the subspace of each class, so it can be used for classification.
4) In order to deal with the problem of face recognition with one sample per person, a method called sub-block principle component analysis (PCA) based on partitions of the sample is presented in this disstertation. It first divides the sample into a few sub-blocks which have equal size and are non-overlapping, and then treats all the sub-blocks as a new sample set. Finally, PCA is performed on all the sub-blocks so as to extract features. Classification is done according to the projection coefficients of sub-blocks of a person.
引文
[1]Ortega G J,Bigun J,Reynolds D.Authentication gets personal with biometrics[J].IEEE Signal Processing Magazine,2004,21(2):50-62.
[2]Zhang D.Automated biometrics:Technologies and systems.2000:Kluwer Acdemic
[3]Jain a K,Ross A,Prabhakar S.An introduction to biometric recognition[J].IEEE Transactions on Circuits and Systems for Video Technology,2004,14(1):4-20.
[4]祝磊.基于子空间分析的人脸识别研究.2007,浙江大学:杭州.
[5]Yang M H,Kriegman D J,Ahuja N.Face Detection in images:A survey[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2002,24(1):34-58.
[6]Hjelma S E,Low B K.Face detection:A survey[J].Computer Vision and Image Understanding,2001,83(3):236-274.
[7]Zhao W,Chellappa R,Phillips P J.Face recognition:A literature survey[J].Acm Computing Surveys,2003,35(4):399-459.
[8]Samal A,Iyengar P.Automatic recognition and analysis of human faces and facial expressions:A survey[J].Pattern Recognition,1992,25:65-77.
[9]Jain A,Lin H,Bolle R.On-line fingerprint verification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(4):302-314.
[10]Maltoni D,Maio D,Jain a K.Handbook of fingerprint recognition.2003:Springer-Verlag.
[11]Wildes R P.Iris recognition:An emerging biometric technology[A].Proceedings of The IEEE[C].1997,85:1348-1363.
[12]Daugrnan J.How iris recognition works[J].IEEE Transactions on Circuits and Systems for Video Technology,2004,14(1):21-30.
[13]Tabatabaee H,Fard a M,Jafariani H.A model human identifier system using retina image and fuzzy clustering[J].Information and Communication Technologies,2006,1:1031-1036.
[14]Faundez Z M.Signature recognition state-of-the-art[J].IEEE Aerospace and Electronic Systems Magazine,,2005,20(7):28-32.
[15]Nalwa V S.Automatic on-line signature verification[J].IEEE/IAFE/INFORMS Conference on Computational Intelligence for Financial Engineering,1997,85:215-239.
[16]Mozaffari S,Faez K,Faradji F.One dimensional fractal coder for online signature recognition[J].International Conference on Pattern Recognition,2006,2:857-860.
[17]Nakano S,Tsubaki T,Hironaka S.Applying a voice recognition system for sf 6 gas insulated switchgear's inspection/maintenance services[J].IEEE Transactions on Power Delivery,2001,16(4):534-538.
[18]Martin J C D,Garcia J M R,Zapata J L G.Robust voice recognition as a distributed service[J].IEEE International Conference on Emerging Technologies and Factory Automation,2001,2:571-575.
[19]Furui S.Recent advances in speaker recognition[J].Pattern Recognition Letters,1997,18(9):859-872.
[20]Guido R C.Introduction to the special issue:Advances on Pattern Recognition for Speech and Audio Processing[J].Pattern Recognition Letters, 2007, 28(11): 1283-1284.
[21] Hashemi J, Fatemizadeh E. Biometric identification through hand geometry[A]. The International Conference on Computer as a Tool[C]. 2005,2:1011-1014.
[22] Sanchez-Reillo R, Sanchez-Avila C, Gonzalez-Marcos A. Biometric identification through hand geometry measurements. [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(10): 1168-117.
[23] Jing X Y, Zhang D. A face and palmprint recognition approach based on discriminant DCT feature extraction[J]. IEEE Transactions on Systems, Man and Cybernetics, 2004, 34(6): 2405-2415.
[24] Zhang D, Kin K W, You J, et al. Online palmprint identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(9):1041-1050.
[25] Nakamoto T, Ozawa T, Shibata Y. Improvement of odor recognition chip[J].IEEE Sensors 2003, 2: 1203-1208.
[26] Ludermir T B, Yamazaki A. Neural networks for odor recognition in artificial noses[A]. International Joint Conference on Neural Networks[C].2003, 1:143-148.
[27] Kusumoputro B, Irwanto P, Jatmiko W. Optimization of fuzzy-neural structure through genetic algorithms and its application in artificial odor recognition-system. [A]. Asia-Pacific Conference on Circuits and Systems [C]. 2002, 2:47-51.
[28] Li Y, Zhi-Chun M, Yu Z, et al. Ear recognition using improved non-negative matrix factorization[A]. International Conference on Pattern Recognition[C]. 2006, 4:501-504.
[29] Hurley D J, M S N, Carter J N. Automatic ear recognition by force field transformations[J]. IEE Colloquium on Visual Biometrics, 2000,1: 1-5.
[30] Hui C, Bir B. Human ear recognition in 3D[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,29(4): 718-737.
[31] Mantyjarvi J, Koivumaki J, Vuori P. Keystroke recognition for virtual keyboard[A]. IEEE International Conference on Multimedia and Expo[C].2002, 2:429-432.
[32] Lanitis A, Cootes T F. An Automatic Face Identification System Using Flexible Appearance Models[J]. Image and Vision Computing, 1995, 13(5):393-401.
[33] Campadelli P, Lanzarotti R, Lipori G. Face localization in color images with complex background[A]. Proceedings of the IEEE International Workshop on Computer Architecture for Machine Perception[C], Palermo. 2005,
[34] Gourier N, Hall D, Crowley J L. Estimating face orientation from robust detection of salient facial features[A]. Visual Observation of Deictic Gestures[C], Cambridge. 2004,
[35] Mckenna S J, Gong S. Real-time face pose estimation[J]. Real-Time Imaging, 1998,4:333-347.
[36] Heinzmann J, Zelinsky A. 3D facial pose and gaze point estimation using a robust real-time tracking paradigm[A]. International Workshop on Automatic Face and Gesture Recognition[C], Nara. 1998, 142-147.
[37] Xiao J, Kanade T, Cohn J F. Robust full-motion recovery of head by dynamic template and re-registration techniques[A]. IEEE International Conference on Automatic Face and Gesture Recognition[C], Washington,DC. 2002, 593-600.
[38]Kanade T,Cohn J,Tian Y.Comprehensive Database for Facial Expression Analysis[A].IEEE International Conference on Face and Gesture Recognition[C].2000,
[39]Pantic M,Rothkrantz L J.Automatic Analysis of Facial Expressions:The State of the Art[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(12):1424-1445.
[40]Jain A,Huang J.Integrating independent components and linear discriminant analysis for gender classification[A].Automatic Face and Gesture Recognition[C].2004,159-163.
[41]Moghaddam B,Yang M.Gender classification with support vector machines[A].Automatic Face and Gesture Recognition[C].2000,306-311.
[42]孙宁等.人脸检测综述[J].电路与系统学报,2006,11(6):101-108.
[43]何坤.人脸识别理论关键技术的研究.2006,四川大学:成都.
[44]Terrillon J C,Shirazi M N,Fukamachi H.Comparative Performance of Different Skin Chrominance Models and Chrominance Spaces for The Automatic Detection of Human Faces in Color Images[A].Automatic Face and Gesture Recognition[C],Grenoble,France.2000,54-61.
[45]Jones M J,Rehg J M.Statistical Color Models with Application to Skin Detection[A].IEEE Conference on Computer Vision and Pattern Recognition[C],Fort Collins,Colorado.1999,274-280.
[46]刘明宝等.彩色图像的实时人脸跟踪方法[J].计算机学报,1998,21(6):527-532.
[47]Yoo T W,Oh I S.A fast algorithm for tracking human faces based on chromatic histograms[J].Pattern Recognition Letters,1999,20(10):967-978.
[48]艾海舟等.基于肤色和模板的人脸检测[J].软件学报,2001,12(12):1784-1792.
[49]Chai D,Ngan K N.Locating Facial Region of a Head-and-Shoulders Color Image[A].Automatic Face and Gesture Recognition[C].1998,124-129.
[50]Jones M J,Rehg J M.Statistical color models with application to skin detection[J].International Journal of Computer Vision archive,2002,46:81-96.
[51]Hsu R L,Mohamed a M,Jain K A.Face Detection in Color Images[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2002,24(5):696-706.
[52]凌旭峰等.彩色序列图像的人脸检测和识别系统[J].电子学报,2003,31(4):544-547.
[53]Karlekar J,Desai U B.Finding Faces in Color Images using Wavelet Transform.[A].IEEE Conf.on Image Analysis and Processing[C],Venice,Italy.1999,1085-1088.
[54]Cai J,Goshtasby A.Detecting human faces in color images[J].Image and Vision Computering,1999,18(1):63-75.
[55]Kim S H,Kim H G.Face Detection using Multi-Model Information[A].Proc Conference on Automatic Face and Gesture Recognition[C],Grenoble,France.2000,70-76.
[56]Miao J,Yin B C,Wang.K Q,et al.A Hierarchical Multi-scale and Mutli-angle System for Human Faces Template in a Complex Background Using Gravity-Center Template[J].Pattern Recognition,1999,32(7):1237-124.
[57]Lee S Y,Ham Y K,Park R H.Recognition of Human Front Faces Using Knowledge-Based Feature Extraction and Neuro-Fuzzy Algorithm[J].Pattern Recognition,1996,29(11):1863-1876.
[58]Lee C H,Kim J S,Park K H.Automatic Human Face Location in a Complex Background Using Motion and Color Imformation[J].Pattern Recogniton,1996,29(11):1877-1889.
[59]Yang G Z,Huang T S.Human Face Detection in a Complex Background[J].Pattern Recognition,1994,277(1):53-63.
[60]卢春雨等.基于区域特征的快速人脸检测法[J].清华大学学报(自然科学版)1999,39(1):101-105.
[61]Jeng S H,Liao H Y M,Han C C.Facial Feature Detection Using Geometrical Face Model:An Efficient Approach[J].Pattern Recognition,,1998,31(3):272-282.
[62]Lv X G,Zhou J,Zhang C S.A Novel Algorithm for Rotated Human Face Detection[A].IEEE Conf.on Computer Vision and Pattern Recognition[C],South Carolina,USA.2000,760-764.
[63]Sung K,Poggio T.Example-Based Learning for View Based Human Face Detection[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,1998,20(1):39-51.
[64]Turk M,Pentland A.Eigenfaces for recognition[J].Journal Cognitive Neuroscience,1991,3(1):71-86.
[65]Moghaddam B,Pentland A.Probabilistic Visual Learning for Object Recognition[J].IEEE Trans on Pattern Analysis and Machine Intelligence,1997,8(7):696-710.
[66]Yang M H,Ahuja N,Kriegman D.Mixtures of Linear Subspaces for Face Detection[A].Automatic Face and Gesture Recognition[C].2000,70-76.
[67]Rowley H A,Baluja S,Kanade T.Rotation Invariant Neural Network-based Face Detection[A].IEEE Conference on Computer Vision and Pattern Recognition[C].1998,38-44.
[68]Rowley H A,Baluja S,Kanade T.Neural Network-Based Face Detection[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,,1998,20(1):23-38.
[69]Garcia C,Delakis M.A Neural Architecture for Fast and Robust Face Detection[A].International Conference on Pattern Recognition[C].2002,2:44-47.
[70]梁路宏等.基于模板匹配与人工神经网络确认的人脸检测[J].电子学报,2001,29(6):744-747.
[71]Nefian a V,Hayes M H.Face detection and recognition using hidden Markov models[A].IEEE Conference on Image Processing[C],Chicago.1998,141-145.
[72]Nefian a V,Hayes M H.An embedden HMM based approach for face detection and recognition[A].IEEE Conference on Acoustics,Speech,and Signal Processing[C],Phoenix.1999,3553-3556.
[73]陈茂林等.自组织隐马尔可夫模型的人脸检测研究[J].计算机学报,2002,25(11):1165-1169.
[74]Vapnik V.The Nature of Statistical Learning Theory.1995,New York:Springer-Verlag.
[75]Osuna E,Freund R,Girosi.F.Training support vector machines:An application to face detection[A].Proc.Computer Vision and Pattern Recognition[C].1997,130-136.
[76]Platt J C.Sequential minimal optimization:A fast algorithm for training support vector machines.1998,MSR.
[77]Viola P.Rapid object detection using a Boosted cascade of simple features[A].Proc IEEE Conference on Computer Vision and Pattern Recognition[C].2001,511-518.
[78]Li S Z,Zhang Z Q.FloatBoost learning and statistical face detection[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,2004,28(9):1112-1123.
[79]Abate a F,Nappi M,Riccio D,et al.2D and 3D face recognition:A survey[J].Pattern Recognition Letters,2007 28:1885-1906.
[80]Bledsoe W W.Man-machine facial recognition[J].Panoramic Research Inc,1966
[81]Kelly M D.Visual identification of people by computer[J].Stanford AI Project,1971
[82]Brunelli R,Poggio T.Face recognition:Feature versus templates[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,1993,15(10):1042-1052.
[83]刘青山等.综述人脸识别中的子空间方法[J].自动化学报,2003,29(6):900-911.
[84]Kirby M,Sirovich L.Application of the Karhunen2Loeve procedure for the charaterization of human faces[J].I EEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(1):103-108.
[85]Swets D L,Weng J Y.Using discriminant eigenfeatures for image retrieval[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8):831-836.
[86]Belhumeur P,Hespanha J,Kriegman D.Eigenfaces vs.Fisherfaces:recognition using class specific linear projection[J].IEEE Trans.Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
[87]Fukunaga K.Introduction to statistical pattern recognition.1990:Academic Press.
[88]Duda R O,Hart P E,Stork.D G.Pattern Classification(Second Edition).2004,Bejing:China Machine Press.
[89]Chen L-F,Liao H-Y M,Ko M-T,et al.A new LDA-based face recognition system which can solve the small sample size problem[J].Pattern Recognition,2000,33(10):1713-1726.
[90]Rui H,Qingshan L,Hanqing L,et al.Solving the small sample size problem of LDA[A].International Conference on Pattern Recognition[C].2002,3:29-32.
[91]Yu H,Yang J.A direct LDA algorithm for high-dimensional data with application to face recognition[J].Pattern Recognition,2001,34(10):2067-2070.
[92]Bartlett M S,Movellan J R,Ejnowski T J.Face recognition by independent component analysis[J].IEEE Transactions On Neural Networks,2002,13(6):1450-1464.
[93]Back K,Draper B A,Beveridge J R.PCA vs.ICA:A comparison on the FERET data set[A].International Conference on Computer Vision Pattern Recognition and Image[C].2002,824-827.
[94]Scholkopf B,Smola A,Muller K R.Nonlinear component analysis as a kernel eigenvalue problem[J].Neural Computation,1998,10(5):1299-1319.
[95]Yang M H,Ahuja N,Kriegman D.Face recognition using kernel eigenfaces[A]. Proceedings of International Conference on Image Processing [C], Vancouver, Canada. 2000, 37-40.
[96] Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach[J]. Neural Computation, 2000, 12(10): 2385-2404.
[97] Mika S, Ratsch G, Weston J. Fisher discriminant analysis with kernels[A].Proceedings of Neural Networks for Signal Processing Workshop[C],Madison, USA 1999, 41-48.
[98] Yang M H. Kernel Eigenfaces vs Kernel Fisherfaces : Face recognition using kernel methods[A]. Proceedings of International Conference on Automatic Face and Gesture Recognition [C], Washington DC, USA.2002,215-220.
[99] Seung H S, Lee D D. The Manifold Ways of Perception[J]. Science, 2000,290: 2268-2269.
[100] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science, 2000,290: 2323-2326.
[101] Tenenbaum J B, Silva V D, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction[J]. Science, 2000,290: 2319-2322.
[102] Cox T, Cox M. Multidimensional Scaling. 1994, London: Chapman&Hall.
[103] Belkin M, Niyogi P. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering[A]. Advances in Neural Information Processing System 15[C], Vancouver, British Columbia, Canada. 2001,
[104] He X, Niyogi P. Locality Preserving Projections[J]. Advances in Neural Information Processing Systems, 2003
[105] He X, Yang S, Hu Y, et al. Face Recognition Using Laplacianfaces[J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.
[106] Biederman I. Recognition by components : A theory of human images understanding. [J]. Psychol, 1987, 94(2): 115-147.
[107] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(21): 788-791.
[108] Lee D D, Seung H S. Algorithms for non2negative matrix factorization[A].Proceedings of Neural Information Processing Systems[C], Denver,USA.2000, 556-562.
[109] Li S Z, Hou X W, Zhang H J, et al. Learning spatially localized , parts-based representation[A]. Proceedings of Computer Vision and Pattern Recognition[C], Hawaii, USA. 2001,
[110] Yang J, Zhang D, Frangi a F, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1):131-137.
[111] Kohonen T. Self-organization and associative memory. 1988:Springer-Verlag.
[112] Cottrell G W, Fleming M K. Face recognition using unsupervised feature extraction[A]. International Neural Network Conference[C]. 1990, 322-325.
[113] Lawrence S, Giles C L, A C T, et al. Face Recognition: A Convolutional Neural-Network Approach[J]. IEEE Transaction on Neural Network, 1997,8(1): 98-113.
[114] Lin S H, Kung S Y, Lin L J. Face recognition/detection by probabilistic decision-based nerual network.[J]. IEEE Transactions on Neural Networks,1997,8(1): 114-132.
[115] Meng J E, Shiqian W, Juwei L, et al. Face Recognition With Radial Basis Function(RBF) Neural Networks[J]. IEEE Transaction on Neural Networks,2002, 13(3): 697-710.
[116] Deever a T, Hemami S S. Efficient sign coding and estimation of zero-quantized coefficients in embedded wavelet image codecs[J]. IEEE Transactions on Image Processing, 2003,12(4): 420-430.
[117] Grgic S, Kers K, Grgic M. Image compression using wavelets[A]. IEEE International Symposium on Industrial Electronics[C]. 1999,1:99-104.
[118] Lazar D, Averbuch A. Wavelet-based video coder via bit allocation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(7):815-832.
[119] Graps A. An introduction to wavelets[J]. Computational Science and Engineering, 1995, 2(2): 50-61.
[120] Lades M, Vorbruggen J, Buhmann J, et al. Distortion invariant object recognition in the dynamic link architecture[J]. IEEE Trans. on Computers,1993, 42(3): 300-311.
[121] Wiskott L, Fellous J M. Face Recognition by Elastic Bunch Graph Matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 775-779.
[122] Liu C J. Gabor-based kernel PCA with fractional power polynomial models for face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(5): 572-581.
[123] Kim S H, Kim H G. Facial region detection using range color information[J].IEICE Trans. Inform. Syst., 1998, E81-D(9): 968-975.
[124] Wang Y-J, Yuan B-Z. Face detection from color images using an evolutionary approach[J]. Chinese Journal of Electronics, 2000, 9(3):270-273.
[125] Andrew R. Statistical Pattern Recognition Second Edition. 2002: Elsevier Academic Press.
[126] Bezdek J C. Pattern recognition with fuzzy objective function. 1981, New York: Plenum Press.
[127] Gath I, Geva A. Unsupervised optimal fuzzy clustering[J]. IEEE trans. on Pattern Analysis and Machine Intelligence, 1989, 7: 773-781.
[128] Yager R R, Filev D P. Generation of fuzzy rules by mountain clustering[J].Intell. Fuzzy Syst., 1994, 2(209-219)
[129] Chiu S L. Fuzzy model identification based on cluster estimation[J]. Intell.Fuzzy Syst., 1994, 2: 267-278.
[130] Krishnapuram R, Kim J. Clustering Algorithms on Volume Criteria[J]. IEEE Trans. Fuzzy Systems, 2000, 8(2): 228-236.
[131] Hadjili M L, Wertz V. Takagi-Sugeno fuzzy modeling incorporating input variables selection[J]. IEEE Trans. Fuzzy System, 2002, 10(6): 728-742.
[132] Gonzalez R C, Woods R E. Digital Image Processing. 2003, Beijing:Electronics Industry Press.
[133] 苏德矿.微积分.2004,北京:高等教育出版社.
[134] Jackway P T, Deriche M. Scale-space Properties of Multiscale Morphological Dilation-Erosion[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 1996, 18(1): 38-51.
[135] Saul L K, Roweis S T. Think Globally, Fit Locally:Unsupervised Learning of Low Dimensional Manifolds[J]. Machine Learning Research, 2003, 4:119-155.
[136] Chen H T, Chang H W, Liu T L. Local Discriminant Embedding and Its Variants[A]. IEEE. Computer Society Conference on Computer Vision and Pattern Recognition[C], San Diego. 2005,2:846-853.
[137] Zhang Z, Zha H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment[J]. SIAM Journal of Scientific Computing,2004, 26(1): 313-338.
[138] Gao Q-B, Wang Z-Z. Center-based Nearest Neighbor Classifier[J]. Pattern Recognition Letters, 2007,40(1): 346-349.
[139] Martinez a M, Kak a C. PCA versus LDA[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2001, 23(2): 228-233.
[140] Chang Y, Hu C, Turk M. Manifold of Facial Expression[A]. Proc. IEEE International Workshop on Analysis and Modeling of Faces and Gestures[C],France. 2003,
[141] 许天周.应用范函分析.2002,北京:科学出版社.
[142] David C L. Linear algebra and its applications. 2003: Pearson Education Press.
[143] Bishop C M. Neural networks for pattern recognition. 1995, New York:Oxford University Press.
[144] Tan X, Chen S C, Zhou Z H, et al. Face recognition from a single image per person: A survey [J]. Pattern Recognition, 2006, 39(9):1725-1745.
[145] Wu J X, Zhou Z H. Face Recognition with one training image per person [J].Pattern Recognition Letters, 2002, 23(14): 1711-1719.
[146] Chen S C, Zhang D Q, Zhou Z H. Enhanced (PC)2A for face recognition with one training image per person [J]. Pattern Recognition Letters, 2004,25(10): 1173-1181.
[147] Wang J, Plataniotis K N, Venetsanopoulos a N. Selecting discriminant eigenfaces for face recognition[J]. Pattern Recognition Letters, 2005, 26(10):1470-1482.
[148] Chen S C, Liu J, Zhou Z H. Making FLDA applicable to face recognition with one sample per person [J]. Pattern Recognition, 2004, 37(7):1553-1555.
[2]Zhang D.Automated biometrics:Technologies and systems.2000:Kluwer Acdemic
[3]Jain a K,Ross A,Prabhakar S.An introduction to biometric recognition[J].IEEE Transactions on Circuits and Systems for Video Technology,2004,14(1):4-20.
[4]祝磊.基于子空间分析的人脸识别研究.2007,浙江大学:杭州.
[5]Yang M H,Kriegman D J,Ahuja N.Face Detection in images:A survey[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2002,24(1):34-58.
[6]Hjelma S E,Low B K.Face detection:A survey[J].Computer Vision and Image Understanding,2001,83(3):236-274.
[7]Zhao W,Chellappa R,Phillips P J.Face recognition:A literature survey[J].Acm Computing Surveys,2003,35(4):399-459.
[8]Samal A,Iyengar P.Automatic recognition and analysis of human faces and facial expressions:A survey[J].Pattern Recognition,1992,25:65-77.
[9]Jain A,Lin H,Bolle R.On-line fingerprint verification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(4):302-314.
[10]Maltoni D,Maio D,Jain a K.Handbook of fingerprint recognition.2003:Springer-Verlag.
[11]Wildes R P.Iris recognition:An emerging biometric technology[A].Proceedings of The IEEE[C].1997,85:1348-1363.
[12]Daugrnan J.How iris recognition works[J].IEEE Transactions on Circuits and Systems for Video Technology,2004,14(1):21-30.
[13]Tabatabaee H,Fard a M,Jafariani H.A model human identifier system using retina image and fuzzy clustering[J].Information and Communication Technologies,2006,1:1031-1036.
[14]Faundez Z M.Signature recognition state-of-the-art[J].IEEE Aerospace and Electronic Systems Magazine,,2005,20(7):28-32.
[15]Nalwa V S.Automatic on-line signature verification[J].IEEE/IAFE/INFORMS Conference on Computational Intelligence for Financial Engineering,1997,85:215-239.
[16]Mozaffari S,Faez K,Faradji F.One dimensional fractal coder for online signature recognition[J].International Conference on Pattern Recognition,2006,2:857-860.
[17]Nakano S,Tsubaki T,Hironaka S.Applying a voice recognition system for sf 6 gas insulated switchgear's inspection/maintenance services[J].IEEE Transactions on Power Delivery,2001,16(4):534-538.
[18]Martin J C D,Garcia J M R,Zapata J L G.Robust voice recognition as a distributed service[J].IEEE International Conference on Emerging Technologies and Factory Automation,2001,2:571-575.
[19]Furui S.Recent advances in speaker recognition[J].Pattern Recognition Letters,1997,18(9):859-872.
[20]Guido R C.Introduction to the special issue:Advances on Pattern Recognition for Speech and Audio Processing[J].Pattern Recognition Letters, 2007, 28(11): 1283-1284.
[21] Hashemi J, Fatemizadeh E. Biometric identification through hand geometry[A]. The International Conference on Computer as a Tool[C]. 2005,2:1011-1014.
[22] Sanchez-Reillo R, Sanchez-Avila C, Gonzalez-Marcos A. Biometric identification through hand geometry measurements. [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(10): 1168-117.
[23] Jing X Y, Zhang D. A face and palmprint recognition approach based on discriminant DCT feature extraction[J]. IEEE Transactions on Systems, Man and Cybernetics, 2004, 34(6): 2405-2415.
[24] Zhang D, Kin K W, You J, et al. Online palmprint identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(9):1041-1050.
[25] Nakamoto T, Ozawa T, Shibata Y. Improvement of odor recognition chip[J].IEEE Sensors 2003, 2: 1203-1208.
[26] Ludermir T B, Yamazaki A. Neural networks for odor recognition in artificial noses[A]. International Joint Conference on Neural Networks[C].2003, 1:143-148.
[27] Kusumoputro B, Irwanto P, Jatmiko W. Optimization of fuzzy-neural structure through genetic algorithms and its application in artificial odor recognition-system. [A]. Asia-Pacific Conference on Circuits and Systems [C]. 2002, 2:47-51.
[28] Li Y, Zhi-Chun M, Yu Z, et al. Ear recognition using improved non-negative matrix factorization[A]. International Conference on Pattern Recognition[C]. 2006, 4:501-504.
[29] Hurley D J, M S N, Carter J N. Automatic ear recognition by force field transformations[J]. IEE Colloquium on Visual Biometrics, 2000,1: 1-5.
[30] Hui C, Bir B. Human ear recognition in 3D[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,29(4): 718-737.
[31] Mantyjarvi J, Koivumaki J, Vuori P. Keystroke recognition for virtual keyboard[A]. IEEE International Conference on Multimedia and Expo[C].2002, 2:429-432.
[32] Lanitis A, Cootes T F. An Automatic Face Identification System Using Flexible Appearance Models[J]. Image and Vision Computing, 1995, 13(5):393-401.
[33] Campadelli P, Lanzarotti R, Lipori G. Face localization in color images with complex background[A]. Proceedings of the IEEE International Workshop on Computer Architecture for Machine Perception[C], Palermo. 2005,
[34] Gourier N, Hall D, Crowley J L. Estimating face orientation from robust detection of salient facial features[A]. Visual Observation of Deictic Gestures[C], Cambridge. 2004,
[35] Mckenna S J, Gong S. Real-time face pose estimation[J]. Real-Time Imaging, 1998,4:333-347.
[36] Heinzmann J, Zelinsky A. 3D facial pose and gaze point estimation using a robust real-time tracking paradigm[A]. International Workshop on Automatic Face and Gesture Recognition[C], Nara. 1998, 142-147.
[37] Xiao J, Kanade T, Cohn J F. Robust full-motion recovery of head by dynamic template and re-registration techniques[A]. IEEE International Conference on Automatic Face and Gesture Recognition[C], Washington,DC. 2002, 593-600.
[38]Kanade T,Cohn J,Tian Y.Comprehensive Database for Facial Expression Analysis[A].IEEE International Conference on Face and Gesture Recognition[C].2000,
[39]Pantic M,Rothkrantz L J.Automatic Analysis of Facial Expressions:The State of the Art[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(12):1424-1445.
[40]Jain A,Huang J.Integrating independent components and linear discriminant analysis for gender classification[A].Automatic Face and Gesture Recognition[C].2004,159-163.
[41]Moghaddam B,Yang M.Gender classification with support vector machines[A].Automatic Face and Gesture Recognition[C].2000,306-311.
[42]孙宁等.人脸检测综述[J].电路与系统学报,2006,11(6):101-108.
[43]何坤.人脸识别理论关键技术的研究.2006,四川大学:成都.
[44]Terrillon J C,Shirazi M N,Fukamachi H.Comparative Performance of Different Skin Chrominance Models and Chrominance Spaces for The Automatic Detection of Human Faces in Color Images[A].Automatic Face and Gesture Recognition[C],Grenoble,France.2000,54-61.
[45]Jones M J,Rehg J M.Statistical Color Models with Application to Skin Detection[A].IEEE Conference on Computer Vision and Pattern Recognition[C],Fort Collins,Colorado.1999,274-280.
[46]刘明宝等.彩色图像的实时人脸跟踪方法[J].计算机学报,1998,21(6):527-532.
[47]Yoo T W,Oh I S.A fast algorithm for tracking human faces based on chromatic histograms[J].Pattern Recognition Letters,1999,20(10):967-978.
[48]艾海舟等.基于肤色和模板的人脸检测[J].软件学报,2001,12(12):1784-1792.
[49]Chai D,Ngan K N.Locating Facial Region of a Head-and-Shoulders Color Image[A].Automatic Face and Gesture Recognition[C].1998,124-129.
[50]Jones M J,Rehg J M.Statistical color models with application to skin detection[J].International Journal of Computer Vision archive,2002,46:81-96.
[51]Hsu R L,Mohamed a M,Jain K A.Face Detection in Color Images[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2002,24(5):696-706.
[52]凌旭峰等.彩色序列图像的人脸检测和识别系统[J].电子学报,2003,31(4):544-547.
[53]Karlekar J,Desai U B.Finding Faces in Color Images using Wavelet Transform.[A].IEEE Conf.on Image Analysis and Processing[C],Venice,Italy.1999,1085-1088.
[54]Cai J,Goshtasby A.Detecting human faces in color images[J].Image and Vision Computering,1999,18(1):63-75.
[55]Kim S H,Kim H G.Face Detection using Multi-Model Information[A].Proc Conference on Automatic Face and Gesture Recognition[C],Grenoble,France.2000,70-76.
[56]Miao J,Yin B C,Wang.K Q,et al.A Hierarchical Multi-scale and Mutli-angle System for Human Faces Template in a Complex Background Using Gravity-Center Template[J].Pattern Recognition,1999,32(7):1237-124.
[57]Lee S Y,Ham Y K,Park R H.Recognition of Human Front Faces Using Knowledge-Based Feature Extraction and Neuro-Fuzzy Algorithm[J].Pattern Recognition,1996,29(11):1863-1876.
[58]Lee C H,Kim J S,Park K H.Automatic Human Face Location in a Complex Background Using Motion and Color Imformation[J].Pattern Recogniton,1996,29(11):1877-1889.
[59]Yang G Z,Huang T S.Human Face Detection in a Complex Background[J].Pattern Recognition,1994,277(1):53-63.
[60]卢春雨等.基于区域特征的快速人脸检测法[J].清华大学学报(自然科学版)1999,39(1):101-105.
[61]Jeng S H,Liao H Y M,Han C C.Facial Feature Detection Using Geometrical Face Model:An Efficient Approach[J].Pattern Recognition,,1998,31(3):272-282.
[62]Lv X G,Zhou J,Zhang C S.A Novel Algorithm for Rotated Human Face Detection[A].IEEE Conf.on Computer Vision and Pattern Recognition[C],South Carolina,USA.2000,760-764.
[63]Sung K,Poggio T.Example-Based Learning for View Based Human Face Detection[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,1998,20(1):39-51.
[64]Turk M,Pentland A.Eigenfaces for recognition[J].Journal Cognitive Neuroscience,1991,3(1):71-86.
[65]Moghaddam B,Pentland A.Probabilistic Visual Learning for Object Recognition[J].IEEE Trans on Pattern Analysis and Machine Intelligence,1997,8(7):696-710.
[66]Yang M H,Ahuja N,Kriegman D.Mixtures of Linear Subspaces for Face Detection[A].Automatic Face and Gesture Recognition[C].2000,70-76.
[67]Rowley H A,Baluja S,Kanade T.Rotation Invariant Neural Network-based Face Detection[A].IEEE Conference on Computer Vision and Pattern Recognition[C].1998,38-44.
[68]Rowley H A,Baluja S,Kanade T.Neural Network-Based Face Detection[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,,1998,20(1):23-38.
[69]Garcia C,Delakis M.A Neural Architecture for Fast and Robust Face Detection[A].International Conference on Pattern Recognition[C].2002,2:44-47.
[70]梁路宏等.基于模板匹配与人工神经网络确认的人脸检测[J].电子学报,2001,29(6):744-747.
[71]Nefian a V,Hayes M H.Face detection and recognition using hidden Markov models[A].IEEE Conference on Image Processing[C],Chicago.1998,141-145.
[72]Nefian a V,Hayes M H.An embedden HMM based approach for face detection and recognition[A].IEEE Conference on Acoustics,Speech,and Signal Processing[C],Phoenix.1999,3553-3556.
[73]陈茂林等.自组织隐马尔可夫模型的人脸检测研究[J].计算机学报,2002,25(11):1165-1169.
[74]Vapnik V.The Nature of Statistical Learning Theory.1995,New York:Springer-Verlag.
[75]Osuna E,Freund R,Girosi.F.Training support vector machines:An application to face detection[A].Proc.Computer Vision and Pattern Recognition[C].1997,130-136.
[76]Platt J C.Sequential minimal optimization:A fast algorithm for training support vector machines.1998,MSR.
[77]Viola P.Rapid object detection using a Boosted cascade of simple features[A].Proc IEEE Conference on Computer Vision and Pattern Recognition[C].2001,511-518.
[78]Li S Z,Zhang Z Q.FloatBoost learning and statistical face detection[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,2004,28(9):1112-1123.
[79]Abate a F,Nappi M,Riccio D,et al.2D and 3D face recognition:A survey[J].Pattern Recognition Letters,2007 28:1885-1906.
[80]Bledsoe W W.Man-machine facial recognition[J].Panoramic Research Inc,1966
[81]Kelly M D.Visual identification of people by computer[J].Stanford AI Project,1971
[82]Brunelli R,Poggio T.Face recognition:Feature versus templates[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,1993,15(10):1042-1052.
[83]刘青山等.综述人脸识别中的子空间方法[J].自动化学报,2003,29(6):900-911.
[84]Kirby M,Sirovich L.Application of the Karhunen2Loeve procedure for the charaterization of human faces[J].I EEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(1):103-108.
[85]Swets D L,Weng J Y.Using discriminant eigenfeatures for image retrieval[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8):831-836.
[86]Belhumeur P,Hespanha J,Kriegman D.Eigenfaces vs.Fisherfaces:recognition using class specific linear projection[J].IEEE Trans.Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
[87]Fukunaga K.Introduction to statistical pattern recognition.1990:Academic Press.
[88]Duda R O,Hart P E,Stork.D G.Pattern Classification(Second Edition).2004,Bejing:China Machine Press.
[89]Chen L-F,Liao H-Y M,Ko M-T,et al.A new LDA-based face recognition system which can solve the small sample size problem[J].Pattern Recognition,2000,33(10):1713-1726.
[90]Rui H,Qingshan L,Hanqing L,et al.Solving the small sample size problem of LDA[A].International Conference on Pattern Recognition[C].2002,3:29-32.
[91]Yu H,Yang J.A direct LDA algorithm for high-dimensional data with application to face recognition[J].Pattern Recognition,2001,34(10):2067-2070.
[92]Bartlett M S,Movellan J R,Ejnowski T J.Face recognition by independent component analysis[J].IEEE Transactions On Neural Networks,2002,13(6):1450-1464.
[93]Back K,Draper B A,Beveridge J R.PCA vs.ICA:A comparison on the FERET data set[A].International Conference on Computer Vision Pattern Recognition and Image[C].2002,824-827.
[94]Scholkopf B,Smola A,Muller K R.Nonlinear component analysis as a kernel eigenvalue problem[J].Neural Computation,1998,10(5):1299-1319.
[95]Yang M H,Ahuja N,Kriegman D.Face recognition using kernel eigenfaces[A]. Proceedings of International Conference on Image Processing [C], Vancouver, Canada. 2000, 37-40.
[96] Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach[J]. Neural Computation, 2000, 12(10): 2385-2404.
[97] Mika S, Ratsch G, Weston J. Fisher discriminant analysis with kernels[A].Proceedings of Neural Networks for Signal Processing Workshop[C],Madison, USA 1999, 41-48.
[98] Yang M H. Kernel Eigenfaces vs Kernel Fisherfaces : Face recognition using kernel methods[A]. Proceedings of International Conference on Automatic Face and Gesture Recognition [C], Washington DC, USA.2002,215-220.
[99] Seung H S, Lee D D. The Manifold Ways of Perception[J]. Science, 2000,290: 2268-2269.
[100] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science, 2000,290: 2323-2326.
[101] Tenenbaum J B, Silva V D, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction[J]. Science, 2000,290: 2319-2322.
[102] Cox T, Cox M. Multidimensional Scaling. 1994, London: Chapman&Hall.
[103] Belkin M, Niyogi P. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering[A]. Advances in Neural Information Processing System 15[C], Vancouver, British Columbia, Canada. 2001,
[104] He X, Niyogi P. Locality Preserving Projections[J]. Advances in Neural Information Processing Systems, 2003
[105] He X, Yang S, Hu Y, et al. Face Recognition Using Laplacianfaces[J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.
[106] Biederman I. Recognition by components : A theory of human images understanding. [J]. Psychol, 1987, 94(2): 115-147.
[107] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(21): 788-791.
[108] Lee D D, Seung H S. Algorithms for non2negative matrix factorization[A].Proceedings of Neural Information Processing Systems[C], Denver,USA.2000, 556-562.
[109] Li S Z, Hou X W, Zhang H J, et al. Learning spatially localized , parts-based representation[A]. Proceedings of Computer Vision and Pattern Recognition[C], Hawaii, USA. 2001,
[110] Yang J, Zhang D, Frangi a F, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1):131-137.
[111] Kohonen T. Self-organization and associative memory. 1988:Springer-Verlag.
[112] Cottrell G W, Fleming M K. Face recognition using unsupervised feature extraction[A]. International Neural Network Conference[C]. 1990, 322-325.
[113] Lawrence S, Giles C L, A C T, et al. Face Recognition: A Convolutional Neural-Network Approach[J]. IEEE Transaction on Neural Network, 1997,8(1): 98-113.
[114] Lin S H, Kung S Y, Lin L J. Face recognition/detection by probabilistic decision-based nerual network.[J]. IEEE Transactions on Neural Networks,1997,8(1): 114-132.
[115] Meng J E, Shiqian W, Juwei L, et al. Face Recognition With Radial Basis Function(RBF) Neural Networks[J]. IEEE Transaction on Neural Networks,2002, 13(3): 697-710.
[116] Deever a T, Hemami S S. Efficient sign coding and estimation of zero-quantized coefficients in embedded wavelet image codecs[J]. IEEE Transactions on Image Processing, 2003,12(4): 420-430.
[117] Grgic S, Kers K, Grgic M. Image compression using wavelets[A]. IEEE International Symposium on Industrial Electronics[C]. 1999,1:99-104.
[118] Lazar D, Averbuch A. Wavelet-based video coder via bit allocation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(7):815-832.
[119] Graps A. An introduction to wavelets[J]. Computational Science and Engineering, 1995, 2(2): 50-61.
[120] Lades M, Vorbruggen J, Buhmann J, et al. Distortion invariant object recognition in the dynamic link architecture[J]. IEEE Trans. on Computers,1993, 42(3): 300-311.
[121] Wiskott L, Fellous J M. Face Recognition by Elastic Bunch Graph Matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 775-779.
[122] Liu C J. Gabor-based kernel PCA with fractional power polynomial models for face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(5): 572-581.
[123] Kim S H, Kim H G. Facial region detection using range color information[J].IEICE Trans. Inform. Syst., 1998, E81-D(9): 968-975.
[124] Wang Y-J, Yuan B-Z. Face detection from color images using an evolutionary approach[J]. Chinese Journal of Electronics, 2000, 9(3):270-273.
[125] Andrew R. Statistical Pattern Recognition Second Edition. 2002: Elsevier Academic Press.
[126] Bezdek J C. Pattern recognition with fuzzy objective function. 1981, New York: Plenum Press.
[127] Gath I, Geva A. Unsupervised optimal fuzzy clustering[J]. IEEE trans. on Pattern Analysis and Machine Intelligence, 1989, 7: 773-781.
[128] Yager R R, Filev D P. Generation of fuzzy rules by mountain clustering[J].Intell. Fuzzy Syst., 1994, 2(209-219)
[129] Chiu S L. Fuzzy model identification based on cluster estimation[J]. Intell.Fuzzy Syst., 1994, 2: 267-278.
[130] Krishnapuram R, Kim J. Clustering Algorithms on Volume Criteria[J]. IEEE Trans. Fuzzy Systems, 2000, 8(2): 228-236.
[131] Hadjili M L, Wertz V. Takagi-Sugeno fuzzy modeling incorporating input variables selection[J]. IEEE Trans. Fuzzy System, 2002, 10(6): 728-742.
[132] Gonzalez R C, Woods R E. Digital Image Processing. 2003, Beijing:Electronics Industry Press.
[133] 苏德矿.微积分.2004,北京:高等教育出版社.
[134] Jackway P T, Deriche M. Scale-space Properties of Multiscale Morphological Dilation-Erosion[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 1996, 18(1): 38-51.
[135] Saul L K, Roweis S T. Think Globally, Fit Locally:Unsupervised Learning of Low Dimensional Manifolds[J]. Machine Learning Research, 2003, 4:119-155.
[136] Chen H T, Chang H W, Liu T L. Local Discriminant Embedding and Its Variants[A]. IEEE. Computer Society Conference on Computer Vision and Pattern Recognition[C], San Diego. 2005,2:846-853.
[137] Zhang Z, Zha H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment[J]. SIAM Journal of Scientific Computing,2004, 26(1): 313-338.
[138] Gao Q-B, Wang Z-Z. Center-based Nearest Neighbor Classifier[J]. Pattern Recognition Letters, 2007,40(1): 346-349.
[139] Martinez a M, Kak a C. PCA versus LDA[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2001, 23(2): 228-233.
[140] Chang Y, Hu C, Turk M. Manifold of Facial Expression[A]. Proc. IEEE International Workshop on Analysis and Modeling of Faces and Gestures[C],France. 2003,
[141] 许天周.应用范函分析.2002,北京:科学出版社.
[142] David C L. Linear algebra and its applications. 2003: Pearson Education Press.
[143] Bishop C M. Neural networks for pattern recognition. 1995, New York:Oxford University Press.
[144] Tan X, Chen S C, Zhou Z H, et al. Face recognition from a single image per person: A survey [J]. Pattern Recognition, 2006, 39(9):1725-1745.
[145] Wu J X, Zhou Z H. Face Recognition with one training image per person [J].Pattern Recognition Letters, 2002, 23(14): 1711-1719.
[146] Chen S C, Zhang D Q, Zhou Z H. Enhanced (PC)2A for face recognition with one training image per person [J]. Pattern Recognition Letters, 2004,25(10): 1173-1181.
[147] Wang J, Plataniotis K N, Venetsanopoulos a N. Selecting discriminant eigenfaces for face recognition[J]. Pattern Recognition Letters, 2005, 26(10):1470-1482.
[148] Chen S C, Liu J, Zhou Z H. Making FLDA applicable to face recognition with one sample per person [J]. Pattern Recognition, 2004, 37(7):1553-1555.