复杂背景下快速多姿态人脸检测研究
|
摘要
人脸检测在人机交互、基于内容的检索、数字视频压缩、视频监控等许多领域具有广阔的应用前景,是各种人脸处理系统最为基础而又十分重要的技术环节,是近年来国内外研究的一个热点。然而目前复杂背景下的多姿态人脸检测还存在很大困难,有效的方法还不多。
本文主要研究了复杂背景下的多姿态人脸检测及相关的快速算法。首先以提高人脸检测速度为主线,研究了复杂背景下人脸检测的各个环节,提出了一些快速算法;然后分别设计了灰度图像和彩色图像的快速多姿态人脸检测方案。具体说来,做了以下工作:
第一,针对人脸检测的各个环节,提出了一些相应的快速算法。针对模板匹配前的灰度分布标准化较为耗时的问题,提出了一种基于广义积分图像的灰度分布标准化快速算法,使图像窗口灰度均值和方差的计算时间大大减少;为综合利用图像梯度特征及梯度方向特征进行人脸检测,采用多方向Kirsch边缘检测算子,并就此提出了一种基于模板分解和积分图像的快速Kirsch边缘检测算法,以解决多方向边缘检测耗时问题;积分投影和方差投影是进行人脸检测和器官定位的常用方法,就此提出了一种基于广义行-列积分图像的快速投影算法,有效地降低了图像窗口行(或列)积分投影和方差投影的计算量;另外,针对多模板匹配的计算效率问题,提出了基于广义积分图像的快速多模板匹配算法;针对模板匹配时存在掩膜的情形,提出了基于广义掩膜积分图像的快速模板匹配算法,并说明了其在多姿态模板匹配中的应用。
第二,针对复杂背景下灰度图像中的多姿态人脸检测问题,提出了一种基于知识模型和模板的快速多姿态人脸检测算法。即首先从原始图像中提取人脸器官梯度图,并建立多姿态知识模型和多姿态模板;然后以多姿态知识模型和知识规则进行人脸粗检,以多姿态模板匹配进行人脸细检,从而得到人脸在图像中的位置和大小信息,并通过眼嘴重心构成的三角形估计人脸的粗略姿态。多姿态知识模型中任意矩形区域的器官梯度特征点快速求和利用积分图像来实现;多姿态模板与图像窗口的局部和整体匹配应用本文提出的快速多姿态模板匹配算法来实现。
第三,针对复杂背景下彩色图像中的多姿态人脸检测问题,提出了一种基于多阈值特征融合的快速多姿态人脸检测算法。即首先从原始图像中,根据人脸器官梯度特征和Kirsch边缘检测算子提取多阈值器官梯度图和梯度方向图,并根据人脸肤色特征提取双阈值肤色图,根据亮度信息提取灰度特征图;然后建立特征融合模型,并应用多姿态知识模型和多姿态模板实现人脸检测。人脸检测过程中,采用了由粗至精的检测策略,并在各个环节应用相关的快速算法,以提高人脸检测的速度;融合了能在复杂背景下区分人脸的多种特征,以提高人脸检测的准确性,还融合了同一特征在不同阈值下的信息,以减小人脸漏检的可能性。另外,根据人眼能不依赖肤色从灰度图像中轻松地分辨出人脸的事实,允许梯度特征具有明显人脸模式的窗口跳过肤色检测直接进入下一级人脸分类器,以减少肤色失真造成的漏检。
Face detection can be widely used in many fields such as human-machine interaction, content-based retrieval, digital video compression and video surveillance. Face detection, which is also a hot topic in domestic and foreign research areas in recent years, is the most basic and very important technique for all sorts of face processing systems. However, there are still difficulties for multi-pose face detection under complex background, and only a few effective methods can be found at present.
This dissertation focuses on multi-pose face detection under complex background and some correlative fast algorithms. Following the main clue of improving face detection speed, this dissertation has studied each tache of multi-pose face detection under complex background, and proposed some fast algorithms. Then the schemes for fast multi-pose face detection in gray and color image are designed respectively. Actually, this dissertation makes some detail contributions as follow:
Firstly, some correlative fast algorithms are presented, aiming at each tache of face detection. As it is very time consuming for grayscale distribution normalization before template matching, a fast algorithm is presented for grayscale distribution normalization based on extended integral image. This algorithm reduces greatly the time for calculating the grayscale average and variance of image windows. In order to make full use of the image grads and grads direction feature for face detection, multi-direction edge detection operators of Kirsch is used. Therefore, a fast algorithm of Kirsch edge detection based on templates decomposition and integral image is proposed for solving the time consuming problem of multi-direction edge detection. Since face detection and organ locating with integral projection and variance projection are in common use, a fast projection algorithm based on extended row-column integral image is presented, and then the calculating time is reduced effectively for the row (or column) integral projection and variance projection of image windows. In addition, aimed at improving the efficiency of multi-template matching, a fast multi-template matching algorithm based on extended integral image is presented. For application of template matching with mask, a fast template matching algorithm based on extended integral image with mask is proposed, and the detailed application in multi-pose face template matching is illustrated.
Secondly, aimed at solving the problem of detecting multi-pose face in gray image under complex background, a fast face detection algorithm is proposed based on multi-pose knowledge models and templates. At first, a face organ grads image is extracted from the original image, and multi-pose knowledge models and the multi-pose face templates are set up. Then coarse face detection is implemented by multi-pose knowledge models and correlative rules, and fine face detection is done by multi-pose face template matching. Finally the position and size of each face are captured in image, and the coarse pose of each face is estimated by the triangle with center of gravity of eyes and mouth. The organ grads feature pixels in random rectangle areas is summed up rapidly by integral image in multi-pose knowledge models. The local and whole matching between multi-pose templates and image windows is implemented by fast multi-pose templates matching algorithm proposed in this dissertation.
Thirdly, aimed at solving the problem of detecting multi-pose face in color image under complex background, a fast multi-pose face detection algorithm based on multi-threshold feature fusion is presented. Multi-threshold organ grads image and grads direction image are extracted by face organ grads feature and Kirsch edge detection operators from the original image, double-threshold skin image is extracted by face skin feature, and gray feature image is extracted by lightness information. Then the feature fusion models are set up, and multi-pose knowledge models and multi-pose face templates are used to accomplish face detection. In face detection processing, coarse-to-fine strategy and correlative fast algorithms in each tache are used to improve the detection speed, several features for distinguishing face under complex background are fused to improve the detection accuracy, and the missed faces are reduced by fusing information of the same feature in different thresholds. In addition, based on the fact that human eyes can detect face easily from gray image without skin color information, windows with obvious grads feature of face pattern are allowed to enter the next face classifier without skin color detection, so the missed faces with skin color distortion can be reduced.
本文主要研究了复杂背景下的多姿态人脸检测及相关的快速算法。首先以提高人脸检测速度为主线,研究了复杂背景下人脸检测的各个环节,提出了一些快速算法;然后分别设计了灰度图像和彩色图像的快速多姿态人脸检测方案。具体说来,做了以下工作:
第一,针对人脸检测的各个环节,提出了一些相应的快速算法。针对模板匹配前的灰度分布标准化较为耗时的问题,提出了一种基于广义积分图像的灰度分布标准化快速算法,使图像窗口灰度均值和方差的计算时间大大减少;为综合利用图像梯度特征及梯度方向特征进行人脸检测,采用多方向Kirsch边缘检测算子,并就此提出了一种基于模板分解和积分图像的快速Kirsch边缘检测算法,以解决多方向边缘检测耗时问题;积分投影和方差投影是进行人脸检测和器官定位的常用方法,就此提出了一种基于广义行-列积分图像的快速投影算法,有效地降低了图像窗口行(或列)积分投影和方差投影的计算量;另外,针对多模板匹配的计算效率问题,提出了基于广义积分图像的快速多模板匹配算法;针对模板匹配时存在掩膜的情形,提出了基于广义掩膜积分图像的快速模板匹配算法,并说明了其在多姿态模板匹配中的应用。
第二,针对复杂背景下灰度图像中的多姿态人脸检测问题,提出了一种基于知识模型和模板的快速多姿态人脸检测算法。即首先从原始图像中提取人脸器官梯度图,并建立多姿态知识模型和多姿态模板;然后以多姿态知识模型和知识规则进行人脸粗检,以多姿态模板匹配进行人脸细检,从而得到人脸在图像中的位置和大小信息,并通过眼嘴重心构成的三角形估计人脸的粗略姿态。多姿态知识模型中任意矩形区域的器官梯度特征点快速求和利用积分图像来实现;多姿态模板与图像窗口的局部和整体匹配应用本文提出的快速多姿态模板匹配算法来实现。
第三,针对复杂背景下彩色图像中的多姿态人脸检测问题,提出了一种基于多阈值特征融合的快速多姿态人脸检测算法。即首先从原始图像中,根据人脸器官梯度特征和Kirsch边缘检测算子提取多阈值器官梯度图和梯度方向图,并根据人脸肤色特征提取双阈值肤色图,根据亮度信息提取灰度特征图;然后建立特征融合模型,并应用多姿态知识模型和多姿态模板实现人脸检测。人脸检测过程中,采用了由粗至精的检测策略,并在各个环节应用相关的快速算法,以提高人脸检测的速度;融合了能在复杂背景下区分人脸的多种特征,以提高人脸检测的准确性,还融合了同一特征在不同阈值下的信息,以减小人脸漏检的可能性。另外,根据人眼能不依赖肤色从灰度图像中轻松地分辨出人脸的事实,允许梯度特征具有明显人脸模式的窗口跳过肤色检测直接进入下一级人脸分类器,以减少肤色失真造成的漏检。
Face detection can be widely used in many fields such as human-machine interaction, content-based retrieval, digital video compression and video surveillance. Face detection, which is also a hot topic in domestic and foreign research areas in recent years, is the most basic and very important technique for all sorts of face processing systems. However, there are still difficulties for multi-pose face detection under complex background, and only a few effective methods can be found at present.
This dissertation focuses on multi-pose face detection under complex background and some correlative fast algorithms. Following the main clue of improving face detection speed, this dissertation has studied each tache of multi-pose face detection under complex background, and proposed some fast algorithms. Then the schemes for fast multi-pose face detection in gray and color image are designed respectively. Actually, this dissertation makes some detail contributions as follow:
Firstly, some correlative fast algorithms are presented, aiming at each tache of face detection. As it is very time consuming for grayscale distribution normalization before template matching, a fast algorithm is presented for grayscale distribution normalization based on extended integral image. This algorithm reduces greatly the time for calculating the grayscale average and variance of image windows. In order to make full use of the image grads and grads direction feature for face detection, multi-direction edge detection operators of Kirsch is used. Therefore, a fast algorithm of Kirsch edge detection based on templates decomposition and integral image is proposed for solving the time consuming problem of multi-direction edge detection. Since face detection and organ locating with integral projection and variance projection are in common use, a fast projection algorithm based on extended row-column integral image is presented, and then the calculating time is reduced effectively for the row (or column) integral projection and variance projection of image windows. In addition, aimed at improving the efficiency of multi-template matching, a fast multi-template matching algorithm based on extended integral image is presented. For application of template matching with mask, a fast template matching algorithm based on extended integral image with mask is proposed, and the detailed application in multi-pose face template matching is illustrated.
Secondly, aimed at solving the problem of detecting multi-pose face in gray image under complex background, a fast face detection algorithm is proposed based on multi-pose knowledge models and templates. At first, a face organ grads image is extracted from the original image, and multi-pose knowledge models and the multi-pose face templates are set up. Then coarse face detection is implemented by multi-pose knowledge models and correlative rules, and fine face detection is done by multi-pose face template matching. Finally the position and size of each face are captured in image, and the coarse pose of each face is estimated by the triangle with center of gravity of eyes and mouth. The organ grads feature pixels in random rectangle areas is summed up rapidly by integral image in multi-pose knowledge models. The local and whole matching between multi-pose templates and image windows is implemented by fast multi-pose templates matching algorithm proposed in this dissertation.
Thirdly, aimed at solving the problem of detecting multi-pose face in color image under complex background, a fast multi-pose face detection algorithm based on multi-threshold feature fusion is presented. Multi-threshold organ grads image and grads direction image are extracted by face organ grads feature and Kirsch edge detection operators from the original image, double-threshold skin image is extracted by face skin feature, and gray feature image is extracted by lightness information. Then the feature fusion models are set up, and multi-pose knowledge models and multi-pose face templates are used to accomplish face detection. In face detection processing, coarse-to-fine strategy and correlative fast algorithms in each tache are used to improve the detection speed, several features for distinguishing face under complex background are fused to improve the detection accuracy, and the missed faces are reduced by fusing information of the same feature in different thresholds. In addition, based on the fact that human eyes can detect face easily from gray image without skin color information, windows with obvious grads feature of face pattern are allowed to enter the next face classifier without skin color detection, so the missed faces with skin color distortion can be reduced.
引文
[1]王蕴红,范伟,谭铁牛.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
[2]王磊,邹北骥,彭小宁,等.一种改进的提取人脸面部特征点的AAM拟合算法.电子学报,2006,34(8):1424-1427
[3]Chai X J,Shan S G.Chen X L,et al.Locally linear regression for pose invariant face recognition.IEEE Transactions on Image Processing,2007,16(7):253-263
[4]Wang F,Wang J D,Zhang C S,James T K.Face recognition using spectral features.Pattern Recognition,2007,40(10):2786-2797
[5]罗希平,田捷.自动指纹识别中的图像增强和细节匹配算法.软件学报,2002,13(5):946-956
[6]解梅,佟异.基于脊线采样的指纹识别算法.电子学报,2003,31(10):1503-1505
[7]D.Wan,J.Zhou.Fingerprint recognition using model-based density map.IEEE Transactions on Image Processing,2006,15(6):1690-1696
[8]Z.Sun,Y.Wang,T.Tan,et al.Improving iris recognition accuracy via cascaded classifiers.IEEE Transactions System Man and Cybernetics,2005,35(3):435-441
[9]黄雅平,罗四维,陈恩义.基于独立分量分析的虹膜识别方法.计算机研究与发展,2003,40(1):1451-1457
[10]王蕴红,朱勇,谭铁牛.基于虹膜识别的身份鉴别.自动化学报,2002,28(1):1-10
[11]李艳来,王宽全,李涛,等.基于平移不变Zernike矩和模块化神经网络的掌纹识别方法.高技术通讯,2005,15(12):19-23
[12]Hua D W,Feng G Y,Zhou Z T.Two-dimensional locality preserving projections with its application to palmprint recognition.Pattern Recognition,2007,40(1):339-342
[13]李文新,夏胜雄,张大鹏,等.基于主线特征的双向匹配的掌纹识别新方法.计算机研究与发展,2004,41(6):996-1002
[14]邬向前,王宽全,张大鹏.一种用于掌纹识别的线特征表示和匹配方法.软 件学报,2004,15(6):869-880
[15]李强,裘正定,孙冬梅,等.基于改进二维主成分分析的在线掌纹识别.电子学报,2005,33(10):1886-1889
[16]王亮,胡卫明,谭铁牛.基于步态的身份识别.计算机学报,2003,26(3):353-360
[17]L.Wang,T.Tan,H.Ning,et al.Fusion of static and dynamic body biometrics for gait recognition.IEEE Transactions Circuits and Systems for Video Technology,2004,14(2):149-158
[18]彭彰,吴晓娟,杨军.基于肢体长度参数的多视角步态识别算法.自动化学报,2007,33(2):210-213
[19]Zheng N H,Tan Lee,Ching P C.Integration of complementary acoustic features for speaker recognition.IEEE Signal Processing Letters,2007,14(3):181-184
[20]Chan W N,Zheng N H,Tan Lee.Discrimination power of vocal source and vocal tract features for speaker recognition.IEEE Transactions on Audio,Speech and Language Processing,2007,15(6):1884-1892
[21]林琳,王树勋.基于自适应小生境混合遗传算法的说话人识别.电子学报,2007,35(1):8-12
[22]Castrillon-Santana M,Deniz-Suarez O,Guerra-Artal C,et al.Real-time detection of multiple faces at different resolutions in video streams.Journal of Visual Communication and Image Representation,2007,18(2):130-140
[23]K(u|¨)blbeck Christian,Ernst Andreas.Face detection and tracking in video sequences using the modifiedcensus transformation.Image & Vision Computing,2006,24(6):564-572
[24]Curran K,Li X,McCaughley N.Neural network face detection.Imaging Science Journal,2005,53(2):105-115
[25]Casas J R,Sitjes A P,Folch P P.Mutual feedback scheme for face detection and tracking aimed at density estimation in demonstrations.IEEE Transactions on Image & Signal Processing,2005,152(3):334-346
[26]Byun Hyeran,Byoungchul Ko.Robust face detection and tracking for real-life applications.International Journal of Pattern Recognition & Artificial Intelligence,2003,17(6):1035-1055
[27]陈锻生,刘政凯.彩色图像边缘特征及其人脸检测性能评价.软件学报,2005,16(5):727-732
[28]Sung K,Poggio T.Example-based learning for view based human face detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1):39-51
[29]Rowley H A,Baluja S,Kanade T.Neural network-based face detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1):23-38
[30]唐伟,陈兆乾,吴建鑫,等.静态灰度图像中的人脸检测方法综述.计算机科学,2002,29(2):134-137
[31]L u X G,Zhou J,Zhang C S.A novel algorithm for rotated human face detection.In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,Hilton Head Island,South Carolina,USA,2000.760-764
[32]卢春雨.人脸自动识别若干问题研究与系统实现:[博士学位论文].北京:清华大学,1998
[33]卢春雨,张长水,闻方,等.基于区域特征的快速人脸检测法.清华大学学报,1999,39(1):101-105
[34]梁路宏,艾海舟,何克忠.基于多模板匹配的单人脸检测.中国图象图形学报,1999,4A(10):823-830
[35]Ai H Z,Liang L H,Xu G Y.A general framework for face detection.In:Tan Tie-Niu,Shi Yuan-Chun,Gao Wen eds.In:Proceedings of the 3rd Conference on Multi-modal Interfaces,Lecture Notes in Computer Science.Berlin:Springer-Verlag,2000.119-126
[36]刑昕,汪孔桥,沈兰荪.基于器官跟踪的人脸实时跟踪方法.电子学报,2000,28(6):29-31
[37]刘明宝,姚鸿勋,高文.彩色图像的实时人脸跟踪方法.计算机学报,1998,21(6):527-532
[38]Wang J G,Tan T N.A new face detection method based on shape information.Pattern Recognition Letters,2000,21(6-7):463-471
[39]Lee P H,Chen C S,Chang W Y.On automatic collection of multiangle face prototypes.IEEE Transactions on Systems,Man & Cybernetics,2006,36(6): 1243-1248
[40]Turk M,Pentland A.Eigenfaces for recognition.Journal of Cognitive Neuro Science,1991,3(1):71-86
[41]Pentland A,Moghaddam B,Starner T.View-based and modular eigenspaces for face recognition.In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,Seattle,WA,1994.21-23
[42]Moghaddam B,Pentland A.Face recognition using view-based and modular eigenspaces.In:Proceedings of SPIE 2257,1994.12-21
[43]Rowley H A,Baluja S,Kanade T.Rotation invariant neural network-based face detection.In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition,1998.38-44
[44]Yang G,Huang T S.Human face detection in complex background.Pattern Recognition 1994,27(1):53-63
[45]Yow K C,Cipolla R.Feature-based human face detection.Image and Vision Computing,1997,15(9):713-735
[46]Han C C,Liao H Y,Yu G J,et al.Fast face detection via morphology-based pre-processing.Pattern Recognition,2000,33(10):1701-1712
[47]Burl M C,Weber M,Perona P.A probabilistic approach to object recognition using local photometry and global geometry.In:Proceedings of European Conference on Computer Vision,1998.628-641
[48]Bruneli R,Poggio T.Face recognition:Features versus templates.IEEE Transactions on pattern analysis and machine intelligence,1993,15(10):1042-1052
[49]梁路宏,艾海舟,徐光祐,等.基于多关联模板匹配与人工神经网确认的人脸检测,电子学报,2001,29(6):744-747
[50]Yullie A,Hallinan P,Cohen D.Feature extraction from faces using deformable templates.International Journal of Computing Vision,1992,8(2):99-111
[51]Yang J,Lu W,Waibel A.Skin color modeling and adaptation.In:Proceedings of the 3rd Asian Conference on Computer Vision,Hong Kong,China,1998.687-694
[52]Storring M,Andersen H,Granum E.Physics-based modelling of human skin under mixed illuminants.Journal of Robotics and Autonomous Systems,2001,35(3-4):131-142
[53]Hiremath P S,Danti Ajit.Detection of multiple faces in an image using skin color information and lines-of-separability face model.International Journal of Pattern Recognition & Artificial Intelligence,2006,20(1):39-61
[54]Kakumanu P,Makrogiannis S,Bourbakis N.A survey of skin-color modeling and detection methods.Pattern Recognition,2007,40(3):1106-1122
[55]Vezhnevets V,Sazonov V,Andreeva A.A survey on pixel-based skin color detection techniques.In:Proceedings of Graphicon-2003,Moscow,Russia,2003.85-92
[56]陈锻生,刘政凯.肤色检测技术综述.计算机学报,2006,29(2):194-207
[57]Yang M H,Ahuja N,Kriegman D.Face detection using mixture of linear subspaces.In:Proceedings of the 4th International Conferences on Automatic Face and Gesture Recognition,2000.70-76
[58]Terrillon J C,Shirazi M N,Sadek M,et al.Invadant face detection with support vector machines.In:Proceedings of the 15th International Conference on Pattern Recognition,2000.vol.4,210-217
[59]梁路宏,艾海舟,肖习攀,等.基于模板匹配与支持矢量机的人脸检测.计算机学报,2002,25(1):22-29
[60]Waring Christopher A,Liu X W.Face detection using spectral histograms and SVMs.IEEE Transactions on Systems,Man & Cybernetics,2005,35(3):467-476
[61]Sahbi Hichem,Geman Donald,Perona Pietro.A hierarchy of support vector machines for pattern detection.Journal of Machine Learning Research,2006,7(10):2087-2123
[62]Buciu I,Kotropoulos C,Pitas I.Demonstrating the stability of support vector machines for classification.Signal Processing,2006,86(9):2364-2380
[63]Kim H,Kang W,Shin J,Park S.Face detection using template matching and ellipse fitting.IEICE Trans.Inf.& Syst.,2000,E38-D(11):2008-2011
[64]Miao J,Yin B C,Wang K Q et al.A hierarchical multiscale and multiangle system for human face detection in a complex back-ground using gravity-center template. Pattern Recognition, 1999, 32(10): 1237-1248
[65] Froba B, Ernst A. Fast frontal-view face detection using a multi-Path decision tree. In: Proceedings of the 4th Internal Conference on Audio- and Video-based Biometric Person Authentication, LNCS 2688,2003. 921-928
[66] Craw I, Ellis H, Lishman J. Automatic extraction of face features. Pattern Recognition Letters, 1987, 5(2): 183-187
[67] Schneiderman H, Kanade T. Probabilistic modeling of local appearance and spatial relationships for object recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, California, 1998.45-51
[68] Liu CJ. A bayesian discriminating features method for face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6):725-740
[69] Rowley H A. Neural network-based face detection. PhD thesis, Carnegie Mellon University, 1999.
[70] Chen Z Y, Qi F H. Cascading neural networks-based human face detection.Journal of Infrared Millim. Waves, 2000,19(1): 57-61
[71] Juell P, Marsh R. A hierarchical neural network for human face detection.Pattern Recognition, 1996,29(5): 781-787
[72] Viola P. Rapid object detection using a boosted cascade of simple features. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, 2001.511-518
[73] Sanderson C, Paliwal K K. Fast features for face authentication under illumination direction changes. Pattern Recognition Letters, 2003, 24(14):2409-2419
[74] Murai K, Nakamura S. Real Time Face Detection for Multimodal Speech Recognition. In: Proceedings of International Conference on Multimedia and Expo, 2002. Vol.2, 373-376
[75] 刘骏.. Delphi数字图像处理及高级应用.北京:科学出版社, 2003
[76] Jiang X Y, Cheng Y L, Xiao R, et al. Spherical harmonic based linear face de-lighting and compensation. Applied Mathematics and Computation, 2007, 185(2):857-868
[77]邢藏菊.人脸自动检测方法研究:[博士学位论文].北京:中国科学院半导体研究所,2001.38-49
[78]郎锐.数字图像处理学VC++实现.北京:北京希望电子出版社,2003
[79]郑永凯,张凌,董守斌.图像灰度分布标准化算法研究.小型微型计算机系统,2002,23(10):1218-1221
[80]梁路宏,艾海舟,何克忠,等.基于多关联模板匹配的人脸检测.软件学报,2001,12(1):94-102
[81]梁路宏,艾海舟,何克忠,等.基于仿射模板匹配的多角度单人脸定位.计算机学报,2000,23(6):640-645
[82]Crow F C.Summed-area tables for texture mapping.Computer Graphics,1984,18(3):207-212
[83]张敏贵.基于小波和支持向量机的人脸识别方法研究:[博士学位论文].西安:西北工业大学,2003.45-47
[84]艾海舟,梁路宏,徐光祐,等.基于肤色和模板的人脸检测.软件学报,2001,12(12):1784-1792
[85]周彩霞,匡纲要,宋海娜,等.用差影法与多模板匹配快速实现人脸检测.计算机应用研究,2004,(5):197-199
[86]Seong-Whan Lee.Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network.IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(6):648-652
[87]H.Li,O.Chutatape.Fundus image features extraction.In:Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society,Chicago,USA,2000.3071-3073
[88]Banumathi A,Devi R K,Raju Kumar V A.Performance analysis of matched filter techniques for automated detection of blood vessels in retinal images.In:Proceedings of the IEEE Conference on Convergent Technologies for Asia-Pacific Region,Bangalore,India,2003.543-546
[89]于烨,陆建华,郑君里.一种新的彩色图像边缘检测算法.清华大学学报,2005,45(10):1339-1343
[90]柏正尧,何佩琨,刘洲峰.基于Kirsch方向模板的SAR相干斑噪声抑制方 法.系统工程与电子技术,2004,26(7):881-882
[91]Kirsch R.Computer determination of the constituent structure of biological images.Computer Biomedical Research,1971,4(3):315-328
[92]卢力,李青,王能超.并行Kirsch算子计算在PVM环境中的实现.信息处理,1997,13(4):363-368
[93]郑翔,黄艺云.Kirsch边缘检测算子的快速算法.通信学报,1996,17(1):131-134
[94]肖温格RA.遥感中的图象处理和分类技术.北京:科学出版社,1991
[95]秦宣云,卜英勇.多方向模板的构造及匹配的快速算法.计算机工程与设计,2003,24(6):91-96
[96]Kanade T.Picture processing by computer complex and recognition of human faces:[Ph.D.Thesis].Kyoto:Kyoto University,1973
[97]Feng G C,Yuen P C.Variance projection function and its application to eye detection for human face recognition.Pattern Recognition Letters,1998,19(9):899-906
[98]耿新,周志华,陈世福.基于混合投影函数的眼睛定位.软件学报,2003,14(8):1394-1400
[99]刘智明,周激流.基于遗传算法的有效人脸检测法.计算机辅助设计与图形学学报,2002,14(10):940-944
[100]马丽红,张宇,余英林.基于改进视网膜抽样模型的人脸检测混合方法.数据采集与处理,2002,17(1):37-41
[101]宋加涛.基于二值边缘图像的眼睛定位和人脸检测:[博士学位论文].杭州:浙江大学,2004
[102]Barnea D I,Silverman H F.A class of algorithms for fast digital image registration.IEEE Transactions on Computer,1972,C21(2):179-186
[103]Vanderbrug G J,Rosenfeld A.Two-stage template matching.IEEE Transactions on Computer,1977,C-26(4):384-393
[104]Vinod V V,Murase H.Focused color intersection with efficient searching for object extraction.Pattern Recognition,1997,30(10):1787-1797
[105]Kawanishi T,Kurozumi T,Kashino K,et al.A fast template matching algorithm with adaptive skipping using inner-subtemplates' distances.In:Proceedings of the 17th International Conference on Pattern Recognition,Cambridge,2004.654-657
[106]Yang M H,Kriegman D,Ahuja N.Detecting faces in images:A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(1):34-58
[107]梁路宏,艾海舟,徐光祐,等.人脸检测研究综述.计算机学报,2002,25(5):449-458
[108]Huang C,Ai H Z,Li Y,et al.High-performance rotation invariant multi-view face detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(4):671-686
[109]Kim H I,Lee S H,Cho N I.Rotation-invariant face detection using angular projections.Electronics Letters,2004,40(12):726-727
[110]方昱春,王蕴红,谭铁牛.融合人脸轮廓和区域信息改进人脸检测.计算机学报,2004,27(4):482-491
[111]Wang K Q.Automatical face detection in images with complex background.In:Proceedings of the IEEE International Conference on Neural Networks and Signal Processing,Nanjing,2003.1027-1030
[112]Zhang Z Q,Zhu L,Li S Z,et al.Real-time multi-view face detection.In:Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition,Washington,2002.20-21
[113]李武军,钟翔平,李宁.一种快速而精确的多人脸检测与定位算法.小型微型计算机系统,2005,26(9):1520-1524
[114]Brown L G.A survey of image registration techniques.ACM Computing Surveys,1992,24(4):325-376
[115]Anisimov V A,Gotsky N D.Fast hierachical matching of an arbitrarily oriented template.Pattern Recognition Letters,1993,14(2):95-101
[116]Kong W Z,Zhu S A.Multi-face detection based on down sampling and modified subtractive clustering for color images.Journal of Zhejiang University Science,2007,8(1):72-78
[117]Rein-Lien H,Abdel-Mottaleb M,Jain A K.Face detection in color images.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):696-706
[118]Wang Y J,Yuan B Z.A novel approach for human face detection from color images under complex background.Pattern Recognition,2001,34(10):1983-1992
[119]Jin Z,Lou Z,Yang J,et al.Face detection using template matching and skin-color information.Neurocomputing,2007,70(4-6):794-800
[120]张明吉,王伟强,郑清芳,等.静态图像中的自适应阈值肤色检测算法.计算机研究与发展,2006,43(9):1674-1680
[121]Abdel-Mottaleb M,Elgammal A.Face detection in complex environments from color images[C].In:Proc IEEE Conference on Image Processing,Kobe,Japan,1999,622-626
[122]陶霖密,彭振云,徐光祐.人体的肤色特征[J].软件学报,2001,12(7):1032-1040
[123]何光宏,潘英俊,吴芳.基于肤色特征和动态聚类的彩色人脸检测.光电工程,2004,31(11):47-50
[124]李晓光,李晓华,沈兰荪.一种基于多级梯度能量特征的DCT压缩域人脸检测算法.电子学报,2005,33(12):2170-2173
[125]章品正,舒华忠.基于小波分析与神经网络的人脸检测方法.电路与系统学报,2007,12(1):55-61
[126]Huang L L,Shimizu Akinobu,Kobatake Hidefumi.Robust face detection using Gabor filter features.Pattern Recognition Letters,2005,26(11):1641-1649
[127]Huang L L,Shimizu Akinobu.A multi-expert approach for robust face detection.Pattern Recognition,2006,39(9):1695-1703
[128]Shih P C,Liu C J.Face detection using discriminating feature analysis and Support Vector Machine.Pattern Recognition,2006,39(2):260-276
[129]田春娜,高新波,李洁.基于嵌入式Bootstrap的主动学习示例选择方法.计算机研究与发展,2006,43(10):1706-1712
[130]Tsalakanidou Filareti,Malassiotis Sotiris,Strintzis Michael G.Face localization and authentication using color and depth images.IEEE Transactions on Image Processing,2005,14(2):152-168
[2]王磊,邹北骥,彭小宁,等.一种改进的提取人脸面部特征点的AAM拟合算法.电子学报,2006,34(8):1424-1427
[3]Chai X J,Shan S G.Chen X L,et al.Locally linear regression for pose invariant face recognition.IEEE Transactions on Image Processing,2007,16(7):253-263
[4]Wang F,Wang J D,Zhang C S,James T K.Face recognition using spectral features.Pattern Recognition,2007,40(10):2786-2797
[5]罗希平,田捷.自动指纹识别中的图像增强和细节匹配算法.软件学报,2002,13(5):946-956
[6]解梅,佟异.基于脊线采样的指纹识别算法.电子学报,2003,31(10):1503-1505
[7]D.Wan,J.Zhou.Fingerprint recognition using model-based density map.IEEE Transactions on Image Processing,2006,15(6):1690-1696
[8]Z.Sun,Y.Wang,T.Tan,et al.Improving iris recognition accuracy via cascaded classifiers.IEEE Transactions System Man and Cybernetics,2005,35(3):435-441
[9]黄雅平,罗四维,陈恩义.基于独立分量分析的虹膜识别方法.计算机研究与发展,2003,40(1):1451-1457
[10]王蕴红,朱勇,谭铁牛.基于虹膜识别的身份鉴别.自动化学报,2002,28(1):1-10
[11]李艳来,王宽全,李涛,等.基于平移不变Zernike矩和模块化神经网络的掌纹识别方法.高技术通讯,2005,15(12):19-23
[12]Hua D W,Feng G Y,Zhou Z T.Two-dimensional locality preserving projections with its application to palmprint recognition.Pattern Recognition,2007,40(1):339-342
[13]李文新,夏胜雄,张大鹏,等.基于主线特征的双向匹配的掌纹识别新方法.计算机研究与发展,2004,41(6):996-1002
[14]邬向前,王宽全,张大鹏.一种用于掌纹识别的线特征表示和匹配方法.软 件学报,2004,15(6):869-880
[15]李强,裘正定,孙冬梅,等.基于改进二维主成分分析的在线掌纹识别.电子学报,2005,33(10):1886-1889
[16]王亮,胡卫明,谭铁牛.基于步态的身份识别.计算机学报,2003,26(3):353-360
[17]L.Wang,T.Tan,H.Ning,et al.Fusion of static and dynamic body biometrics for gait recognition.IEEE Transactions Circuits and Systems for Video Technology,2004,14(2):149-158
[18]彭彰,吴晓娟,杨军.基于肢体长度参数的多视角步态识别算法.自动化学报,2007,33(2):210-213
[19]Zheng N H,Tan Lee,Ching P C.Integration of complementary acoustic features for speaker recognition.IEEE Signal Processing Letters,2007,14(3):181-184
[20]Chan W N,Zheng N H,Tan Lee.Discrimination power of vocal source and vocal tract features for speaker recognition.IEEE Transactions on Audio,Speech and Language Processing,2007,15(6):1884-1892
[21]林琳,王树勋.基于自适应小生境混合遗传算法的说话人识别.电子学报,2007,35(1):8-12
[22]Castrillon-Santana M,Deniz-Suarez O,Guerra-Artal C,et al.Real-time detection of multiple faces at different resolutions in video streams.Journal of Visual Communication and Image Representation,2007,18(2):130-140
[23]K(u|¨)blbeck Christian,Ernst Andreas.Face detection and tracking in video sequences using the modifiedcensus transformation.Image & Vision Computing,2006,24(6):564-572
[24]Curran K,Li X,McCaughley N.Neural network face detection.Imaging Science Journal,2005,53(2):105-115
[25]Casas J R,Sitjes A P,Folch P P.Mutual feedback scheme for face detection and tracking aimed at density estimation in demonstrations.IEEE Transactions on Image & Signal Processing,2005,152(3):334-346
[26]Byun Hyeran,Byoungchul Ko.Robust face detection and tracking for real-life applications.International Journal of Pattern Recognition & Artificial Intelligence,2003,17(6):1035-1055
[27]陈锻生,刘政凯.彩色图像边缘特征及其人脸检测性能评价.软件学报,2005,16(5):727-732
[28]Sung K,Poggio T.Example-based learning for view based human face detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1):39-51
[29]Rowley H A,Baluja S,Kanade T.Neural network-based face detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1):23-38
[30]唐伟,陈兆乾,吴建鑫,等.静态灰度图像中的人脸检测方法综述.计算机科学,2002,29(2):134-137
[31]L u X G,Zhou J,Zhang C S.A novel algorithm for rotated human face detection.In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,Hilton Head Island,South Carolina,USA,2000.760-764
[32]卢春雨.人脸自动识别若干问题研究与系统实现:[博士学位论文].北京:清华大学,1998
[33]卢春雨,张长水,闻方,等.基于区域特征的快速人脸检测法.清华大学学报,1999,39(1):101-105
[34]梁路宏,艾海舟,何克忠.基于多模板匹配的单人脸检测.中国图象图形学报,1999,4A(10):823-830
[35]Ai H Z,Liang L H,Xu G Y.A general framework for face detection.In:Tan Tie-Niu,Shi Yuan-Chun,Gao Wen eds.In:Proceedings of the 3rd Conference on Multi-modal Interfaces,Lecture Notes in Computer Science.Berlin:Springer-Verlag,2000.119-126
[36]刑昕,汪孔桥,沈兰荪.基于器官跟踪的人脸实时跟踪方法.电子学报,2000,28(6):29-31
[37]刘明宝,姚鸿勋,高文.彩色图像的实时人脸跟踪方法.计算机学报,1998,21(6):527-532
[38]Wang J G,Tan T N.A new face detection method based on shape information.Pattern Recognition Letters,2000,21(6-7):463-471
[39]Lee P H,Chen C S,Chang W Y.On automatic collection of multiangle face prototypes.IEEE Transactions on Systems,Man & Cybernetics,2006,36(6): 1243-1248
[40]Turk M,Pentland A.Eigenfaces for recognition.Journal of Cognitive Neuro Science,1991,3(1):71-86
[41]Pentland A,Moghaddam B,Starner T.View-based and modular eigenspaces for face recognition.In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,Seattle,WA,1994.21-23
[42]Moghaddam B,Pentland A.Face recognition using view-based and modular eigenspaces.In:Proceedings of SPIE 2257,1994.12-21
[43]Rowley H A,Baluja S,Kanade T.Rotation invariant neural network-based face detection.In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition,1998.38-44
[44]Yang G,Huang T S.Human face detection in complex background.Pattern Recognition 1994,27(1):53-63
[45]Yow K C,Cipolla R.Feature-based human face detection.Image and Vision Computing,1997,15(9):713-735
[46]Han C C,Liao H Y,Yu G J,et al.Fast face detection via morphology-based pre-processing.Pattern Recognition,2000,33(10):1701-1712
[47]Burl M C,Weber M,Perona P.A probabilistic approach to object recognition using local photometry and global geometry.In:Proceedings of European Conference on Computer Vision,1998.628-641
[48]Bruneli R,Poggio T.Face recognition:Features versus templates.IEEE Transactions on pattern analysis and machine intelligence,1993,15(10):1042-1052
[49]梁路宏,艾海舟,徐光祐,等.基于多关联模板匹配与人工神经网确认的人脸检测,电子学报,2001,29(6):744-747
[50]Yullie A,Hallinan P,Cohen D.Feature extraction from faces using deformable templates.International Journal of Computing Vision,1992,8(2):99-111
[51]Yang J,Lu W,Waibel A.Skin color modeling and adaptation.In:Proceedings of the 3rd Asian Conference on Computer Vision,Hong Kong,China,1998.687-694
[52]Storring M,Andersen H,Granum E.Physics-based modelling of human skin under mixed illuminants.Journal of Robotics and Autonomous Systems,2001,35(3-4):131-142
[53]Hiremath P S,Danti Ajit.Detection of multiple faces in an image using skin color information and lines-of-separability face model.International Journal of Pattern Recognition & Artificial Intelligence,2006,20(1):39-61
[54]Kakumanu P,Makrogiannis S,Bourbakis N.A survey of skin-color modeling and detection methods.Pattern Recognition,2007,40(3):1106-1122
[55]Vezhnevets V,Sazonov V,Andreeva A.A survey on pixel-based skin color detection techniques.In:Proceedings of Graphicon-2003,Moscow,Russia,2003.85-92
[56]陈锻生,刘政凯.肤色检测技术综述.计算机学报,2006,29(2):194-207
[57]Yang M H,Ahuja N,Kriegman D.Face detection using mixture of linear subspaces.In:Proceedings of the 4th International Conferences on Automatic Face and Gesture Recognition,2000.70-76
[58]Terrillon J C,Shirazi M N,Sadek M,et al.Invadant face detection with support vector machines.In:Proceedings of the 15th International Conference on Pattern Recognition,2000.vol.4,210-217
[59]梁路宏,艾海舟,肖习攀,等.基于模板匹配与支持矢量机的人脸检测.计算机学报,2002,25(1):22-29
[60]Waring Christopher A,Liu X W.Face detection using spectral histograms and SVMs.IEEE Transactions on Systems,Man & Cybernetics,2005,35(3):467-476
[61]Sahbi Hichem,Geman Donald,Perona Pietro.A hierarchy of support vector machines for pattern detection.Journal of Machine Learning Research,2006,7(10):2087-2123
[62]Buciu I,Kotropoulos C,Pitas I.Demonstrating the stability of support vector machines for classification.Signal Processing,2006,86(9):2364-2380
[63]Kim H,Kang W,Shin J,Park S.Face detection using template matching and ellipse fitting.IEICE Trans.Inf.& Syst.,2000,E38-D(11):2008-2011
[64]Miao J,Yin B C,Wang K Q et al.A hierarchical multiscale and multiangle system for human face detection in a complex back-ground using gravity-center template. Pattern Recognition, 1999, 32(10): 1237-1248
[65] Froba B, Ernst A. Fast frontal-view face detection using a multi-Path decision tree. In: Proceedings of the 4th Internal Conference on Audio- and Video-based Biometric Person Authentication, LNCS 2688,2003. 921-928
[66] Craw I, Ellis H, Lishman J. Automatic extraction of face features. Pattern Recognition Letters, 1987, 5(2): 183-187
[67] Schneiderman H, Kanade T. Probabilistic modeling of local appearance and spatial relationships for object recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, California, 1998.45-51
[68] Liu CJ. A bayesian discriminating features method for face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6):725-740
[69] Rowley H A. Neural network-based face detection. PhD thesis, Carnegie Mellon University, 1999.
[70] Chen Z Y, Qi F H. Cascading neural networks-based human face detection.Journal of Infrared Millim. Waves, 2000,19(1): 57-61
[71] Juell P, Marsh R. A hierarchical neural network for human face detection.Pattern Recognition, 1996,29(5): 781-787
[72] Viola P. Rapid object detection using a boosted cascade of simple features. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, 2001.511-518
[73] Sanderson C, Paliwal K K. Fast features for face authentication under illumination direction changes. Pattern Recognition Letters, 2003, 24(14):2409-2419
[74] Murai K, Nakamura S. Real Time Face Detection for Multimodal Speech Recognition. In: Proceedings of International Conference on Multimedia and Expo, 2002. Vol.2, 373-376
[75] 刘骏.. Delphi数字图像处理及高级应用.北京:科学出版社, 2003
[76] Jiang X Y, Cheng Y L, Xiao R, et al. Spherical harmonic based linear face de-lighting and compensation. Applied Mathematics and Computation, 2007, 185(2):857-868
[77]邢藏菊.人脸自动检测方法研究:[博士学位论文].北京:中国科学院半导体研究所,2001.38-49
[78]郎锐.数字图像处理学VC++实现.北京:北京希望电子出版社,2003
[79]郑永凯,张凌,董守斌.图像灰度分布标准化算法研究.小型微型计算机系统,2002,23(10):1218-1221
[80]梁路宏,艾海舟,何克忠,等.基于多关联模板匹配的人脸检测.软件学报,2001,12(1):94-102
[81]梁路宏,艾海舟,何克忠,等.基于仿射模板匹配的多角度单人脸定位.计算机学报,2000,23(6):640-645
[82]Crow F C.Summed-area tables for texture mapping.Computer Graphics,1984,18(3):207-212
[83]张敏贵.基于小波和支持向量机的人脸识别方法研究:[博士学位论文].西安:西北工业大学,2003.45-47
[84]艾海舟,梁路宏,徐光祐,等.基于肤色和模板的人脸检测.软件学报,2001,12(12):1784-1792
[85]周彩霞,匡纲要,宋海娜,等.用差影法与多模板匹配快速实现人脸检测.计算机应用研究,2004,(5):197-199
[86]Seong-Whan Lee.Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network.IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(6):648-652
[87]H.Li,O.Chutatape.Fundus image features extraction.In:Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society,Chicago,USA,2000.3071-3073
[88]Banumathi A,Devi R K,Raju Kumar V A.Performance analysis of matched filter techniques for automated detection of blood vessels in retinal images.In:Proceedings of the IEEE Conference on Convergent Technologies for Asia-Pacific Region,Bangalore,India,2003.543-546
[89]于烨,陆建华,郑君里.一种新的彩色图像边缘检测算法.清华大学学报,2005,45(10):1339-1343
[90]柏正尧,何佩琨,刘洲峰.基于Kirsch方向模板的SAR相干斑噪声抑制方 法.系统工程与电子技术,2004,26(7):881-882
[91]Kirsch R.Computer determination of the constituent structure of biological images.Computer Biomedical Research,1971,4(3):315-328
[92]卢力,李青,王能超.并行Kirsch算子计算在PVM环境中的实现.信息处理,1997,13(4):363-368
[93]郑翔,黄艺云.Kirsch边缘检测算子的快速算法.通信学报,1996,17(1):131-134
[94]肖温格RA.遥感中的图象处理和分类技术.北京:科学出版社,1991
[95]秦宣云,卜英勇.多方向模板的构造及匹配的快速算法.计算机工程与设计,2003,24(6):91-96
[96]Kanade T.Picture processing by computer complex and recognition of human faces:[Ph.D.Thesis].Kyoto:Kyoto University,1973
[97]Feng G C,Yuen P C.Variance projection function and its application to eye detection for human face recognition.Pattern Recognition Letters,1998,19(9):899-906
[98]耿新,周志华,陈世福.基于混合投影函数的眼睛定位.软件学报,2003,14(8):1394-1400
[99]刘智明,周激流.基于遗传算法的有效人脸检测法.计算机辅助设计与图形学学报,2002,14(10):940-944
[100]马丽红,张宇,余英林.基于改进视网膜抽样模型的人脸检测混合方法.数据采集与处理,2002,17(1):37-41
[101]宋加涛.基于二值边缘图像的眼睛定位和人脸检测:[博士学位论文].杭州:浙江大学,2004
[102]Barnea D I,Silverman H F.A class of algorithms for fast digital image registration.IEEE Transactions on Computer,1972,C21(2):179-186
[103]Vanderbrug G J,Rosenfeld A.Two-stage template matching.IEEE Transactions on Computer,1977,C-26(4):384-393
[104]Vinod V V,Murase H.Focused color intersection with efficient searching for object extraction.Pattern Recognition,1997,30(10):1787-1797
[105]Kawanishi T,Kurozumi T,Kashino K,et al.A fast template matching algorithm with adaptive skipping using inner-subtemplates' distances.In:Proceedings of the 17th International Conference on Pattern Recognition,Cambridge,2004.654-657
[106]Yang M H,Kriegman D,Ahuja N.Detecting faces in images:A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(1):34-58
[107]梁路宏,艾海舟,徐光祐,等.人脸检测研究综述.计算机学报,2002,25(5):449-458
[108]Huang C,Ai H Z,Li Y,et al.High-performance rotation invariant multi-view face detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(4):671-686
[109]Kim H I,Lee S H,Cho N I.Rotation-invariant face detection using angular projections.Electronics Letters,2004,40(12):726-727
[110]方昱春,王蕴红,谭铁牛.融合人脸轮廓和区域信息改进人脸检测.计算机学报,2004,27(4):482-491
[111]Wang K Q.Automatical face detection in images with complex background.In:Proceedings of the IEEE International Conference on Neural Networks and Signal Processing,Nanjing,2003.1027-1030
[112]Zhang Z Q,Zhu L,Li S Z,et al.Real-time multi-view face detection.In:Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition,Washington,2002.20-21
[113]李武军,钟翔平,李宁.一种快速而精确的多人脸检测与定位算法.小型微型计算机系统,2005,26(9):1520-1524
[114]Brown L G.A survey of image registration techniques.ACM Computing Surveys,1992,24(4):325-376
[115]Anisimov V A,Gotsky N D.Fast hierachical matching of an arbitrarily oriented template.Pattern Recognition Letters,1993,14(2):95-101
[116]Kong W Z,Zhu S A.Multi-face detection based on down sampling and modified subtractive clustering for color images.Journal of Zhejiang University Science,2007,8(1):72-78
[117]Rein-Lien H,Abdel-Mottaleb M,Jain A K.Face detection in color images.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):696-706
[118]Wang Y J,Yuan B Z.A novel approach for human face detection from color images under complex background.Pattern Recognition,2001,34(10):1983-1992
[119]Jin Z,Lou Z,Yang J,et al.Face detection using template matching and skin-color information.Neurocomputing,2007,70(4-6):794-800
[120]张明吉,王伟强,郑清芳,等.静态图像中的自适应阈值肤色检测算法.计算机研究与发展,2006,43(9):1674-1680
[121]Abdel-Mottaleb M,Elgammal A.Face detection in complex environments from color images[C].In:Proc IEEE Conference on Image Processing,Kobe,Japan,1999,622-626
[122]陶霖密,彭振云,徐光祐.人体的肤色特征[J].软件学报,2001,12(7):1032-1040
[123]何光宏,潘英俊,吴芳.基于肤色特征和动态聚类的彩色人脸检测.光电工程,2004,31(11):47-50
[124]李晓光,李晓华,沈兰荪.一种基于多级梯度能量特征的DCT压缩域人脸检测算法.电子学报,2005,33(12):2170-2173
[125]章品正,舒华忠.基于小波分析与神经网络的人脸检测方法.电路与系统学报,2007,12(1):55-61
[126]Huang L L,Shimizu Akinobu,Kobatake Hidefumi.Robust face detection using Gabor filter features.Pattern Recognition Letters,2005,26(11):1641-1649
[127]Huang L L,Shimizu Akinobu.A multi-expert approach for robust face detection.Pattern Recognition,2006,39(9):1695-1703
[128]Shih P C,Liu C J.Face detection using discriminating feature analysis and Support Vector Machine.Pattern Recognition,2006,39(2):260-276
[129]田春娜,高新波,李洁.基于嵌入式Bootstrap的主动学习示例选择方法.计算机研究与发展,2006,43(10):1706-1712
[130]Tsalakanidou Filareti,Malassiotis Sotiris,Strintzis Michael G.Face localization and authentication using color and depth images.IEEE Transactions on Image Processing,2005,14(2):152-168
© 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 |