基于知识方法和自适应模板匹配的快速人脸检测
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摘要
人脸检测就是判断被检图像中的人脸,并确定其大小及位置。人脸检测是人脸识别过程中的必要前期工作,其主要作用是将人脸部分从背景图像中分离出来。一个对一般环境图像具有一定适应能力的人脸识别系统,需要一个鲁棒的、高效的、实时的人脸检测系统。近年来,随着计算机视觉技术和模式识别技术的发展,以及硬件速度的提高和成本的降低,基于生物特征的身份认证技术近年来发展迅速。与其它生物认证技术相比,人脸识别具有更直接、友好、方便的优点,是当前热门研究领域之一。
本文以完成人脸识别前期准备为目标,利用肤色的聚类特性,设计并实现了一个复杂背景下的人脸检测算法。论文包括四方面的内容:源图像的采集、图像预处理、人脸肤色分割、人脸检测。论文首先应用海康威视的图像采集卡,完成了图像采集功能和云台控制功能;然后对实时获取的图像进行图像预处理,在图像预处理阶段依次对图像进行中值滤波、光线补偿、彩色图像的灰度化,并在Rein-Lien Hsu等人的光照补偿算法基础上提出了通过设定门限值来提高光照补偿效果。在肤色分割阶段,本文提出一种非线性变换的肤色聚类和边缘检测相结合的肤色分割算法,该分割算法首先利用非线性变换的肤色聚类性分割出图像中的肤色区域,然后根据各个区域的面积决定是否利用局部边缘信息来继续分割该区域。最后应用人脸检测算法对分割后的候选人脸区域进行检测和验证,本文提出基于知识方法的验证和多种形式的自适应模板检测方法相结合的人脸检测算法,并且对于人脸特征不明显以及部分遮挡的候选人脸区域,应用半人脸模板检测来避免漏检人脸。
Face detection is to determine the size and location of a human face in an image which is being detected. Face detection is the necessary earlier task in face recognition process, aiming at separating the face area from the background image. Face recognition system adapted to some general environmental image to some extent needs a Robust、high efficient and real-time face detection system. In recent years, with the technological development of computer vision and pattern recognition, as well as the improving of hardware speed and cost reducing, the identity authentication technology based on biometrics has developed rapidly .Compared with other biometrics technology, face detection is more direct、much friendlier、and more convenient and it is one of the hot research fields, drawing more and more attention.
This paper sets completing earlier preparation for face recognition as its objection, using skin color clustering, and it has designed and realized a human face detection algorithm under a complex background. This paper includes four aspects: image capture、image preprocessing、segmentation of face skin color and face detection. First, image capture and pan-tilt control are firstly achieved based on HiKivision’s image capture board. And then it comes to preprocessing the real-time images, during which median filter, lighting compensation and image gray processing are to be realized successively, what is more, a threshold value is presented to improve compensation effect on the base of light compensation algorithm of Rein-Lien Hsu etc. During the phase of skin segmentation after pretreatment, a non-linearity transform method of skin color segmentation is proposed, combining skin color clustering and edge detection. It first makes use of the skin color clustering characteristic of non-linearity transform to segment different skin color regions. Then it needs to decide whether local non-linearity information should be applied to go on segmenting the regions depending on their areas. Finally it detects and validates the candidate face region that has been segmented by human face detection algorithm. This paper puts forward a face detection algorithm which combines the detection method based on knowledge and self-adaptive template matching methods. As for the face region where the features are not obvious or some of which is covered, half-face template should be used to avoid missing detection of face region.
本文以完成人脸识别前期准备为目标,利用肤色的聚类特性,设计并实现了一个复杂背景下的人脸检测算法。论文包括四方面的内容:源图像的采集、图像预处理、人脸肤色分割、人脸检测。论文首先应用海康威视的图像采集卡,完成了图像采集功能和云台控制功能;然后对实时获取的图像进行图像预处理,在图像预处理阶段依次对图像进行中值滤波、光线补偿、彩色图像的灰度化,并在Rein-Lien Hsu等人的光照补偿算法基础上提出了通过设定门限值来提高光照补偿效果。在肤色分割阶段,本文提出一种非线性变换的肤色聚类和边缘检测相结合的肤色分割算法,该分割算法首先利用非线性变换的肤色聚类性分割出图像中的肤色区域,然后根据各个区域的面积决定是否利用局部边缘信息来继续分割该区域。最后应用人脸检测算法对分割后的候选人脸区域进行检测和验证,本文提出基于知识方法的验证和多种形式的自适应模板检测方法相结合的人脸检测算法,并且对于人脸特征不明显以及部分遮挡的候选人脸区域,应用半人脸模板检测来避免漏检人脸。
Face detection is to determine the size and location of a human face in an image which is being detected. Face detection is the necessary earlier task in face recognition process, aiming at separating the face area from the background image. Face recognition system adapted to some general environmental image to some extent needs a Robust、high efficient and real-time face detection system. In recent years, with the technological development of computer vision and pattern recognition, as well as the improving of hardware speed and cost reducing, the identity authentication technology based on biometrics has developed rapidly .Compared with other biometrics technology, face detection is more direct、much friendlier、and more convenient and it is one of the hot research fields, drawing more and more attention.
This paper sets completing earlier preparation for face recognition as its objection, using skin color clustering, and it has designed and realized a human face detection algorithm under a complex background. This paper includes four aspects: image capture、image preprocessing、segmentation of face skin color and face detection. First, image capture and pan-tilt control are firstly achieved based on HiKivision’s image capture board. And then it comes to preprocessing the real-time images, during which median filter, lighting compensation and image gray processing are to be realized successively, what is more, a threshold value is presented to improve compensation effect on the base of light compensation algorithm of Rein-Lien Hsu etc. During the phase of skin segmentation after pretreatment, a non-linearity transform method of skin color segmentation is proposed, combining skin color clustering and edge detection. It first makes use of the skin color clustering characteristic of non-linearity transform to segment different skin color regions. Then it needs to decide whether local non-linearity information should be applied to go on segmenting the regions depending on their areas. Finally it detects and validates the candidate face region that has been segmented by human face detection algorithm. This paper puts forward a face detection algorithm which combines the detection method based on knowledge and self-adaptive template matching methods. As for the face region where the features are not obvious or some of which is covered, half-face template should be used to avoid missing detection of face region.
引文
[1] YANG M-H, KRIEGMAN D J, AHUJA N. Detecting faces in images: A survey[J]. IEEE Trans PAMI, 2002, 24(1):34-58.
[2] Wang J G, Tam T N. A new face detection method based on shape information [J].Pattern Recognition Letters, 2000,21(6/7):463 - 471.
[3]姜军,张桂林.一种基于知识的快速人脸检测方法[J].中国图像图形学报(A版) , 2002, 7(1):6-10.
[4]章品正,赵洪玉,梁晓云等.一种复杂背景中的人脸检测与验证方法[J].数据采集与处理, 2004, 19:10-15.
[5] Hsu R L, Mottaleb M A, Jain A K. Face detection in color images[J].IEEE Trans Pattern Analysis and Machine Intelligence , 2002 , 24(5):696 -706.
[6]赵丽红,刘纪红,徐心和.人脸检测方法综述[J].计算机应用研究,2004年第9期.
[7]王延江,袁宗宝,唐晚芳.一种快速彩色图像中复杂背景下人脸检测方法[J].电子学报, 2002, 30(10):1566-1569.
[8] Rein-Lien Hsu,Mohamed Abdel-Mottaleb,Anil K.Jain.Face Detection in Color Images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002 .24(5):696-706.
[9] Ing-Sheen Hsieh, Kuo-Chin Fan,Chiunhsiun Lin. A Statistic Approach to the Detection of Human Faces in Color Nature Scene[J]. Pattern Recognition. 2002, 35(7): 1583-1596.
[10] J.D.Brand, Dr.J.S.D.Mason, A Skin Probability Map and Its Use in Face Detection. Proceedings of International Conferenceon Images Processing[J].2001,1(7-10):1034-1037.
[11]张洪明,赵德斌,高文.基于肤色模型、神经网络和人脸结构模型的平面旋转人脸检测[J],计算机学报,2002,25(11):1250-1256.
[12]赵丽红,刘纪红,徐心和.基于外观的人脸检测方法.计算计应用研究[J], 2004年第9期.
[13]艾海舟,梁路宏,徐光佑等.基于肤色和模板的人脸检测[J].软件学报, 2001, 12(12): 1784-1792.
[14]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[J] .计算机学报, 2002, 25(1): 22-29.
[15] Bai-Bo, Chang-Shui Z. Lower bounds estimation to KL transform in face representation and recognition[A]. Proc of 2002 International Conference on Machine Learning and Cybernetics[C]. 2002. 1314 - 1318.
[16]苏宏涛,张艳宁,王晶,等.光照变化条件下的人脸识别研究[J].西北工业大学学报(自然科学版), 2004, 22(4): 426-430.
[17]穆丽萍,陈定方.基于肤色和支持向量机的人脸检测技术研究[D].武汉理工大学, 2007.
[18] H M El-Bakry, H Stoyan. Fast neural networks for sub-matrix(object/face)detection, Circuits and Systems[A]. ISCAS'04. Proceedings of the 2004 International Symposium[C]. 2004, 5: 764-767.
[19]卢春水,张长水,闻芳,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.
[20] Hyungkeun J, Kyunghee L, Sungbum P. Eye and face detection using SVM[A] . Proc of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference[C],2004. 577 - 580.
[21] P Viola,M Jones.Robust real time object detection[A].8th IEEE International Conference on Computer Vision(ICCV)[C].Vancouver,British Columbia,2001.
[22] S Z Li,Z Q Zhang.FloatBoost learning and statistical face detection[J].IEEE Trans.Pattern Analysis and Machine Intelligence,2004.28(9):1112-1123.
[23] R Xiao,M J Li,H J Zhang.Robust multipose face detection in images[J].IEEE Trans.Circuits and Systems for Video Technology,2004,14(1):31-41.
[24] Rein-Lien Hsu, Mohamed Abel-Mottaleb, Anil K.Jain. Face Detection in Color Images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL.24, No.5, MAY 2002.
[25]谷口庆治.数字图像处理基础篇[M].科学出版社.
[26]余松煜,周源华等.数字图像处理[M].电子工业出版社.
[27]王浩军,郑崇勋等.基于自适应多尺度的血液细胞图像阈值分割方法研究[J].西安交通大学学报,第35卷第4期, 2001.
[28]陶霖密,徐光佑.机器视觉中的颜色问题及应用[J].科学通报, 2001,46(3):177- 189.
[29] Yong Beom Lee, Bum Jae You. A real-time color-based object tracking robust to irregular illumination variations[J]. IEEE Int.Conf.2001,2:1659-1664.
[30]陈柏生,陈锻生.基于归一化rgb彩色模型的运动阴影检测[J].计算机应用.第26卷第8期. 2006年8月.
[31] Elli Angelopoulou. Understanding the Color of Human Skin [C].Proceedings of the SPIE Conference on Human Vision and Electronic Imaging VI (SPIE), SPIE Press, 2001. 243- 251.
[32] DE DIOS J , GARCIA N. Face detection based on a new color space YCgCr [ C ] Proceedings of International Conference on Image Processing. 2003, 2 :9092912.
[33]刘慧敏,,舒宁,林卉.图像边缘信息分析中数学形态学的应用方法[J].武汉大学学报,第26卷第4期, 2001.
[34] Shixiong Li. Multi-faces Location in Color Image[D]. ProQuest学位论文, 2002.
[35]游亚平,袁保宗.复杂背景下人脸检测的数学形态学运算方法[J].电子与信息学报.第26卷第12期, 2004年12月.
[36] Gandoin P M, Devillers O. Progressive Lossless Compression of Arbitrary Simplicial Complexes[J].ACM Trans. on Graphics,2002,21(3):372-379.
[37] Erik Hjelmas, Boon Kee Low. Face Detection: A Survey[J]. Computer Vision and Image Understanding 83,pp236-274,2001.
[38] Krishnan Nallaperumal Ravi Subban. Human Face Detection in Color Images Using Skin Color and Template Matching Models for Multimedia on the Web Wireless and Optical Communications Networks[J],2006 IFIP International Conference on 11-13 April 2006 Page(s):5 pp.
[39]姜凯,陈海霞,刘立峰等.基于模板抽样的快速图像匹配算法[J].光学精密工程, 2004.6 12(3). 311-315.
[40] B.Zitova,J.Flusser. Image Registration methods:a surver[J].Image and Vision Computing.2003,(21):997-1000.
[41] Ming-Hsuan Yang, David Kriegman, and Narendra Ahuja.“Detecting Faces in Images: A Survey”[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), vol.24, no.1, pp.34-58,2002.
[42]王长军,朱善安.基于颜色和变形模板的实时人体检测[J].中国图像图形学报,第11卷,第6期, 2006.
[43]李震,洪添胜,吴伟斌.双线性插值算法在荔枝树光合模拟中叶片图像旋转中的应用农业工程学报[J].第22卷,第11期, 2006.
[44]孔潇,沈兰荪,毋立芳.一种基于特征提取和模板匹配的人脸检测算法[J].测控技术, 2003,第22卷,第5期.
[45]费俊琳,俞王新,王志中.一种改进的基于模板匹配眼睛特征点定位算法[J].计算机应用与工程. 2007,43(32), 207.
[2] Wang J G, Tam T N. A new face detection method based on shape information [J].Pattern Recognition Letters, 2000,21(6/7):463 - 471.
[3]姜军,张桂林.一种基于知识的快速人脸检测方法[J].中国图像图形学报(A版) , 2002, 7(1):6-10.
[4]章品正,赵洪玉,梁晓云等.一种复杂背景中的人脸检测与验证方法[J].数据采集与处理, 2004, 19:10-15.
[5] Hsu R L, Mottaleb M A, Jain A K. Face detection in color images[J].IEEE Trans Pattern Analysis and Machine Intelligence , 2002 , 24(5):696 -706.
[6]赵丽红,刘纪红,徐心和.人脸检测方法综述[J].计算机应用研究,2004年第9期.
[7]王延江,袁宗宝,唐晚芳.一种快速彩色图像中复杂背景下人脸检测方法[J].电子学报, 2002, 30(10):1566-1569.
[8] Rein-Lien Hsu,Mohamed Abdel-Mottaleb,Anil K.Jain.Face Detection in Color Images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002 .24(5):696-706.
[9] Ing-Sheen Hsieh, Kuo-Chin Fan,Chiunhsiun Lin. A Statistic Approach to the Detection of Human Faces in Color Nature Scene[J]. Pattern Recognition. 2002, 35(7): 1583-1596.
[10] J.D.Brand, Dr.J.S.D.Mason, A Skin Probability Map and Its Use in Face Detection. Proceedings of International Conferenceon Images Processing[J].2001,1(7-10):1034-1037.
[11]张洪明,赵德斌,高文.基于肤色模型、神经网络和人脸结构模型的平面旋转人脸检测[J],计算机学报,2002,25(11):1250-1256.
[12]赵丽红,刘纪红,徐心和.基于外观的人脸检测方法.计算计应用研究[J], 2004年第9期.
[13]艾海舟,梁路宏,徐光佑等.基于肤色和模板的人脸检测[J].软件学报, 2001, 12(12): 1784-1792.
[14]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[J] .计算机学报, 2002, 25(1): 22-29.
[15] Bai-Bo, Chang-Shui Z. Lower bounds estimation to KL transform in face representation and recognition[A]. Proc of 2002 International Conference on Machine Learning and Cybernetics[C]. 2002. 1314 - 1318.
[16]苏宏涛,张艳宁,王晶,等.光照变化条件下的人脸识别研究[J].西北工业大学学报(自然科学版), 2004, 22(4): 426-430.
[17]穆丽萍,陈定方.基于肤色和支持向量机的人脸检测技术研究[D].武汉理工大学, 2007.
[18] H M El-Bakry, H Stoyan. Fast neural networks for sub-matrix(object/face)detection, Circuits and Systems[A]. ISCAS'04. Proceedings of the 2004 International Symposium[C]. 2004, 5: 764-767.
[19]卢春水,张长水,闻芳,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.
[20] Hyungkeun J, Kyunghee L, Sungbum P. Eye and face detection using SVM[A] . Proc of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference[C],2004. 577 - 580.
[21] P Viola,M Jones.Robust real time object detection[A].8th IEEE International Conference on Computer Vision(ICCV)[C].Vancouver,British Columbia,2001.
[22] S Z Li,Z Q Zhang.FloatBoost learning and statistical face detection[J].IEEE Trans.Pattern Analysis and Machine Intelligence,2004.28(9):1112-1123.
[23] R Xiao,M J Li,H J Zhang.Robust multipose face detection in images[J].IEEE Trans.Circuits and Systems for Video Technology,2004,14(1):31-41.
[24] Rein-Lien Hsu, Mohamed Abel-Mottaleb, Anil K.Jain. Face Detection in Color Images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL.24, No.5, MAY 2002.
[25]谷口庆治.数字图像处理基础篇[M].科学出版社.
[26]余松煜,周源华等.数字图像处理[M].电子工业出版社.
[27]王浩军,郑崇勋等.基于自适应多尺度的血液细胞图像阈值分割方法研究[J].西安交通大学学报,第35卷第4期, 2001.
[28]陶霖密,徐光佑.机器视觉中的颜色问题及应用[J].科学通报, 2001,46(3):177- 189.
[29] Yong Beom Lee, Bum Jae You. A real-time color-based object tracking robust to irregular illumination variations[J]. IEEE Int.Conf.2001,2:1659-1664.
[30]陈柏生,陈锻生.基于归一化rgb彩色模型的运动阴影检测[J].计算机应用.第26卷第8期. 2006年8月.
[31] Elli Angelopoulou. Understanding the Color of Human Skin [C].Proceedings of the SPIE Conference on Human Vision and Electronic Imaging VI (SPIE), SPIE Press, 2001. 243- 251.
[32] DE DIOS J , GARCIA N. Face detection based on a new color space YCgCr [ C ] Proceedings of International Conference on Image Processing. 2003, 2 :9092912.
[33]刘慧敏,,舒宁,林卉.图像边缘信息分析中数学形态学的应用方法[J].武汉大学学报,第26卷第4期, 2001.
[34] Shixiong Li. Multi-faces Location in Color Image[D]. ProQuest学位论文, 2002.
[35]游亚平,袁保宗.复杂背景下人脸检测的数学形态学运算方法[J].电子与信息学报.第26卷第12期, 2004年12月.
[36] Gandoin P M, Devillers O. Progressive Lossless Compression of Arbitrary Simplicial Complexes[J].ACM Trans. on Graphics,2002,21(3):372-379.
[37] Erik Hjelmas, Boon Kee Low. Face Detection: A Survey[J]. Computer Vision and Image Understanding 83,pp236-274,2001.
[38] Krishnan Nallaperumal Ravi Subban. Human Face Detection in Color Images Using Skin Color and Template Matching Models for Multimedia on the Web Wireless and Optical Communications Networks[J],2006 IFIP International Conference on 11-13 April 2006 Page(s):5 pp.
[39]姜凯,陈海霞,刘立峰等.基于模板抽样的快速图像匹配算法[J].光学精密工程, 2004.6 12(3). 311-315.
[40] B.Zitova,J.Flusser. Image Registration methods:a surver[J].Image and Vision Computing.2003,(21):997-1000.
[41] Ming-Hsuan Yang, David Kriegman, and Narendra Ahuja.“Detecting Faces in Images: A Survey”[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), vol.24, no.1, pp.34-58,2002.
[42]王长军,朱善安.基于颜色和变形模板的实时人体检测[J].中国图像图形学报,第11卷,第6期, 2006.
[43]李震,洪添胜,吴伟斌.双线性插值算法在荔枝树光合模拟中叶片图像旋转中的应用农业工程学报[J].第22卷,第11期, 2006.
[44]孔潇,沈兰荪,毋立芳.一种基于特征提取和模板匹配的人脸检测算法[J].测控技术, 2003,第22卷,第5期.
[45]费俊琳,俞王新,王志中.一种改进的基于模板匹配眼睛特征点定位算法[J].计算机应用与工程. 2007,43(32), 207.