复杂背景中的人脸检测与定位
|
摘要
近些年来，自动人脸检测与识别在图像处理和计算机视觉领域引起许多学者的关注。这一课题在智能监视系统、虚拟现实、高级用户接口、表情分析和基于模型的图像编码等方面具有广阔的应用前景。利用图像或图像序列进行人脸检测与识别包含三个基本内容：(1)从背景中检测定位出人脸；(2)人脸跟踪；(3)人脸的识别和理解。其中，人脸的检测与定位是自动人脸检测与识别过程的关键，是进一步识别和理解人脸的基础。复杂背景中的人脸检测是自动人脸检测与定位的高级阶段，也是近几年来人脸检测研究的热点。本文实现了两种复杂背景中的人脸检测定位算法。
算法Ⅰ基于色彩信息，利用模糊模式匹配的方式检测人脸。该算法由两个部分组成。首先，粗略定出可能存在人脸的区域，即候选人脸区域；然后在候选人脸区域里细致检测以证实人脸的存在并定位证实存在的人脸。算法Ⅰ可检测彩色图像中任意背景，任意姿态(-90°—90°)，多尺度，不同亮度条件下的任意个人脸。
算法Ⅱ基于支持向量机(SVM)，由训练和检测两部分组成。训练过程是用大量人脸样本、“非人脸”样本训练SVM分类器，使之获得一个最优分类超平面。检测阶段用训练好的SVM分类器检测图像中的人脸。算法Ⅱ可检测彩色或灰度图像中任意背景，大致正面姿态，任意表情，多尺度的任意个人脸。
论文分别给出了使用算法Ⅰ、算法Ⅱ检测人脸的实验结果，并对算法Ⅰ与算法Ⅱ进行了检测性能的比较。实验结果证明算法Ⅰ的检测性能优于算法Ⅱ。
Automatic face detection and recognition has been receiving increasing attention from researchers in the fields of image processing and computer vision during the past few years. It has a lot of applications in smart surveillance system, virtual reality, advanced user interface, emotion analysis and model-based coding, etc. Generally speaking, the procedure of the automatic face detection and recognition from images or a sequence of images involves three main stages: (1) face detection and location in a scene; (2) face tracking; (3) face recognition and understanding. As the base of the face detection and recognition, face detection and location is the key of the whole procedure. Face detection in complex backgrounds is the advanced stage of the automatic face detection and location. And it is focus in the recent face detection research. This paper describes two methods of the face detection in complex backgrounds automatically.
Algorithm I is based on color information, and constitutes two sections. First, the technique approximately detects the image positions where the probability of finding a face is high; then the location accuracy of the candidate faces is improved and their existence is verified and marked. It can be used to detect many faces with different
sizes and directions ( - 90 - 90) in a color static image.
Algorithm II is based on support vector machine and made of training section and detecting section. A lot of face samples and "not face" samples are used to train the SVM classifier, to get optimal separating hyperplane in the training. And SVM classifier is used to detect faces in the detecting. This algorithm can be used to detect many frontal faces with different sizes in a color image or gray image.
The paper respectively gives the experimental results of algorithm I and algorithm II, and compares the detection performance of the two algorithms. Experimental results show that algorithm I is better than algorithm II.
算法Ⅰ基于色彩信息，利用模糊模式匹配的方式检测人脸。该算法由两个部分组成。首先，粗略定出可能存在人脸的区域，即候选人脸区域；然后在候选人脸区域里细致检测以证实人脸的存在并定位证实存在的人脸。算法Ⅰ可检测彩色图像中任意背景，任意姿态(-90°—90°)，多尺度，不同亮度条件下的任意个人脸。
算法Ⅱ基于支持向量机(SVM)，由训练和检测两部分组成。训练过程是用大量人脸样本、“非人脸”样本训练SVM分类器，使之获得一个最优分类超平面。检测阶段用训练好的SVM分类器检测图像中的人脸。算法Ⅱ可检测彩色或灰度图像中任意背景，大致正面姿态，任意表情，多尺度的任意个人脸。
论文分别给出了使用算法Ⅰ、算法Ⅱ检测人脸的实验结果，并对算法Ⅰ与算法Ⅱ进行了检测性能的比较。实验结果证明算法Ⅰ的检测性能优于算法Ⅱ。
Automatic face detection and recognition has been receiving increasing attention from researchers in the fields of image processing and computer vision during the past few years. It has a lot of applications in smart surveillance system, virtual reality, advanced user interface, emotion analysis and model-based coding, etc. Generally speaking, the procedure of the automatic face detection and recognition from images or a sequence of images involves three main stages: (1) face detection and location in a scene; (2) face tracking; (3) face recognition and understanding. As the base of the face detection and recognition, face detection and location is the key of the whole procedure. Face detection in complex backgrounds is the advanced stage of the automatic face detection and location. And it is focus in the recent face detection research. This paper describes two methods of the face detection in complex backgrounds automatically.
Algorithm I is based on color information, and constitutes two sections. First, the technique approximately detects the image positions where the probability of finding a face is high; then the location accuracy of the candidate faces is improved and their existence is verified and marked. It can be used to detect many faces with different
sizes and directions ( - 90 - 90) in a color static image.
Algorithm II is based on support vector machine and made of training section and detecting section. A lot of face samples and "not face" samples are used to train the SVM classifier, to get optimal separating hyperplane in the training. And SVM classifier is used to detect faces in the detecting. This algorithm can be used to detect many frontal faces with different sizes in a color image or gray image.
The paper respectively gives the experimental results of algorithm I and algorithm II, and compares the detection performance of the two algorithms. Experimental results show that algorithm I is better than algorithm II.
引文
1． Kinmanlam, Yan H, Locating and extracting the eye in human face images, Pattern Recognition, Vol. 29(5) , pp771-779, 1996．
2． Dario Miao, Davide Maltoni, Real-time face location on gray-scale static images, Pattern Recognition, Vol. 33, pp1525-1539, 2000．
3． S.R. Gunn, M.S. Nixon, Snake head boundary extraction using global and local energy minimization, proceedings of the 13th ICPR.V.B, pp581-585, 1996．
4． Martin H, Hunke H, Locating and tracking of human faces with neural networks, Carnegia Mellon University: Technical Report CMU-CS-94-155, 1994．
5． B. Moghaddam, A. Pentland, Maximum likelihood detection of faces and hands, Proceedings of International Workshop on Automatic Face and Gesture Recognition, pp122-128, 1995．
6． Choong Hwan Lee, Jun Sung Kim, Kyu Ho Park, Automatic Human Face Location in a Complex Backgrounds Using Motion and Color Information, pattern recognition, Vol.29． No. 11 pp1877-1889,1996．
7． Haiyuan Wu, Qian Chen, and Masahike Yachida, Face Detection From Color Images Using a Fuzzy Pattern Matching Method, Pattern Analysis And Machine Intelligence, Vol.21, No.6, June 1999．
8． J.Cai, A.Goshtasby, Detection human faces in color images, image and vision computing, Vol. 18, pp.63-75, 1999．
9． Ming Xu and Takao AKATSUKA, Multi-module method for detection of human face from complex background, SPIE, Vol. 3460, July 1998．
10． Jie Yang, A.Weibel, A real-time face tracker, Proceedings of WACV'96(Sarasota, Floreda,USA),pp142-147．
11． Jian-Gang Wang, Eric Sung, Frontal-view face detection and facial feature extraction using color and morphological operations, Pattern Recognition Letters (20) ,pp1053-1068, 1999．
12． Rama Chellappa, Charles. L. Wilson, Saad Sirohey, Human and Machine Recognition of faces: A Survey, Proceedings of IEEE, Vol.83, NO.5, pp705-740, 1995．
13． Y. H. Kwon and Noda Vitoria Lobo, Face Detection Using Templates, ICPR'94, PP764-767, 1994．
14. Jun Miao, baoCai Yin, Kongqiao Wang, LanSun Shen, Xuecun Chen, "hierarchical multiscale and multiangle system for human face detection in a complex background using gravity-center template, Pattern Recognition Vol.32, pp1237-1248, 1999.
15. Dai, Y, Nakano, Y, Face-texture model based on SGLD and its application in face detection in a color scene, Pattern Recognition 29(6), pp1007-1017.
16. Weeks R, Hague G E. Color Segmentation in the HIS Color Space Using Kmeans Algorithm. SPIE, 3026, pp 143-154, 1997.
17. N.Herodotou, K. N. Plataniotis, A. N. Venetsanopoulos, Automatic location and tracking of the facial region in color video sequences, Signal Processing: Image Communication, Vol. 14, pp359-388, 1999.
18. Eli Saber, A. Murat Tekalp, Frontal-view detection and facial feature extraction using color, shape and symmetry based cost function, Pattern Recognition Letter, Vol. 19, pp669-680, 1998.
19. Vapnik V, Estimation of dependences based on empirical data. Springer-Verlag, 1982.
20. Gunn S R. support vector machines for classification and regression. Technical Report: Image Speech and Intelligent Systems Research group, 1997, University of Southampton.
21. Osuna E, Freund R, Girosi F. Training support vector machines: an application to face detection .In: Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'), 1997.130～136.
22. Platt J C. Sequential minimal optimization: a fast algorithm for training support vector machines. Technical Report: MSR-TR-98-14, 1998.
23．章毓晋，图像处理和分析，清华大学出版社，1999．
24．姚鸿勋，刘明宝，高文，范旭彤，张洪明，吕雅娟，基于彩色图像的色系坐标变换的面部定位与跟踪法，计算机学报，Vol．23 No．2，pp158-165，2000
25．康学雷，邵凌，张立明，一种基于肤色和模板的人脸检测方法，红外与毫米波学报，Vol．19，No．3，pp209-214，2000
26．姚鸿勋，刘明宝，高文，范旭彤，张洪明，吕雅娟，基于彩色图像的色系坐标变换的面部定位与跟踪法，计算机学报，Vol．23，No．2，pp158-165，2000
27．陶霖密，彭震云，徐光祜，人体的肤色特征，软件学报，Vol．12，No．17，pp1032-1041，2001．
28．边肇祺，张学工等，模式识别(第二版)，清华大学出版社，2000．
29．崔伟东，周志华，李星，支持向量机研究，计算机工程与应用，Vol．1，No．1，pp58-61，2001
30．张学工，关于统计学习理论与支持向量机，自动化学报，Vol．26，No．1，pp31-42，2000
2． Dario Miao, Davide Maltoni, Real-time face location on gray-scale static images, Pattern Recognition, Vol. 33, pp1525-1539, 2000．
3． S.R. Gunn, M.S. Nixon, Snake head boundary extraction using global and local energy minimization, proceedings of the 13th ICPR.V.B, pp581-585, 1996．
4． Martin H, Hunke H, Locating and tracking of human faces with neural networks, Carnegia Mellon University: Technical Report CMU-CS-94-155, 1994．
5． B. Moghaddam, A. Pentland, Maximum likelihood detection of faces and hands, Proceedings of International Workshop on Automatic Face and Gesture Recognition, pp122-128, 1995．
6． Choong Hwan Lee, Jun Sung Kim, Kyu Ho Park, Automatic Human Face Location in a Complex Backgrounds Using Motion and Color Information, pattern recognition, Vol.29． No. 11 pp1877-1889,1996．
7． Haiyuan Wu, Qian Chen, and Masahike Yachida, Face Detection From Color Images Using a Fuzzy Pattern Matching Method, Pattern Analysis And Machine Intelligence, Vol.21, No.6, June 1999．
8． J.Cai, A.Goshtasby, Detection human faces in color images, image and vision computing, Vol. 18, pp.63-75, 1999．
9． Ming Xu and Takao AKATSUKA, Multi-module method for detection of human face from complex background, SPIE, Vol. 3460, July 1998．
10． Jie Yang, A.Weibel, A real-time face tracker, Proceedings of WACV'96(Sarasota, Floreda,USA),pp142-147．
11． Jian-Gang Wang, Eric Sung, Frontal-view face detection and facial feature extraction using color and morphological operations, Pattern Recognition Letters (20) ,pp1053-1068, 1999．
12． Rama Chellappa, Charles. L. Wilson, Saad Sirohey, Human and Machine Recognition of faces: A Survey, Proceedings of IEEE, Vol.83, NO.5, pp705-740, 1995．
13． Y. H. Kwon and Noda Vitoria Lobo, Face Detection Using Templates, ICPR'94, PP764-767, 1994．
14. Jun Miao, baoCai Yin, Kongqiao Wang, LanSun Shen, Xuecun Chen, "hierarchical multiscale and multiangle system for human face detection in a complex background using gravity-center template, Pattern Recognition Vol.32, pp1237-1248, 1999.
15. Dai, Y, Nakano, Y, Face-texture model based on SGLD and its application in face detection in a color scene, Pattern Recognition 29(6), pp1007-1017.
16. Weeks R, Hague G E. Color Segmentation in the HIS Color Space Using Kmeans Algorithm. SPIE, 3026, pp 143-154, 1997.
17. N.Herodotou, K. N. Plataniotis, A. N. Venetsanopoulos, Automatic location and tracking of the facial region in color video sequences, Signal Processing: Image Communication, Vol. 14, pp359-388, 1999.
18. Eli Saber, A. Murat Tekalp, Frontal-view detection and facial feature extraction using color, shape and symmetry based cost function, Pattern Recognition Letter, Vol. 19, pp669-680, 1998.
19. Vapnik V, Estimation of dependences based on empirical data. Springer-Verlag, 1982.
20. Gunn S R. support vector machines for classification and regression. Technical Report: Image Speech and Intelligent Systems Research group, 1997, University of Southampton.
21. Osuna E, Freund R, Girosi F. Training support vector machines: an application to face detection .In: Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'), 1997.130～136.
22. Platt J C. Sequential minimal optimization: a fast algorithm for training support vector machines. Technical Report: MSR-TR-98-14, 1998.
23．章毓晋，图像处理和分析，清华大学出版社，1999．
24．姚鸿勋，刘明宝，高文，范旭彤，张洪明，吕雅娟，基于彩色图像的色系坐标变换的面部定位与跟踪法，计算机学报，Vol．23 No．2，pp158-165，2000
25．康学雷，邵凌，张立明，一种基于肤色和模板的人脸检测方法，红外与毫米波学报，Vol．19，No．3，pp209-214，2000
26．姚鸿勋，刘明宝，高文，范旭彤，张洪明，吕雅娟，基于彩色图像的色系坐标变换的面部定位与跟踪法，计算机学报，Vol．23，No．2，pp158-165，2000
27．陶霖密，彭震云，徐光祜，人体的肤色特征，软件学报，Vol．12，No．17，pp1032-1041，2001．
28．边肇祺，张学工等，模式识别(第二版)，清华大学出版社，2000．
29．崔伟东，周志华，李星，支持向量机研究，计算机工程与应用，Vol．1，No．1，pp58-61，2001
30．张学工，关于统计学习理论与支持向量机，自动化学报，Vol．26，No．1，pp31-42，2000