基于人脸图像的性别识别技术研究
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摘要
近年来,随着人脸识别技术研究的深入,人脸性别识别已成为计算机视觉和模式识别领域中的热门研究课题之一。人脸性别识别是人脸图像信息利用计算机技术识别被观察者的性别属性的过程。在人工智能、系统监控、模式识别等方面有着重要的应用前景。
在综合分析国内外主要的特征提取方法和性别识别技术的基础上,本文提出基于独立成分分析和核Fisher判别分析相融合的特征提取算法、改进KNN-SVM的性别识别算法、基于人脸下巴轮廓曲线曲率的性别识别方法,并开发原型系统进行算法验证。具体内容如下:
(1)提出ICA和核Fisher判别分析(KFDA)相结合的特征提取方法。ICA得到的特征信息是更能重构原始数据的信息,Fisher判别分析则能得到相应的最佳分类的特征信息,由于光照、姿态、表情等变化引起人脸图像的差异造成人脸图像的分布往往是非线性的,而Fisher判别分析提取的是图像的线性特征,因此本文采用核Fisher判别分析(KFDA)算法,并用主成分分析(PCA)解决其存在的高时间复杂度问题,经过预处理的图像投影到ICA和KFDA张成的子空间中。
(2)提出加权KNN和SVM相结合的改进KNN-SVM性别识别算法。针对支持向量机(SVM)在超平面附近进行性别识别的不准确性,引入加权K近邻(KNN)算法,融合加权KNN和SVM进行性别识别。该算法用少量已知性别样本自动确定加权KNN与SVM的最优分类阈值,并计算待识别样本和支持向量机所确定的超平面的距离,通过距离与阈值的比较进行性别识别。
(3)提出基于人脸下巴轮廓曲线曲率的性别识别方法。该方法首先利用log-gabor对人脸进行处理,得到更能反映人脸纹理曲线的图像,然后利用AAM算法进行人脸下巴曲线定位,并对得到的人脸下巴特征点进行最小二乘拟合,最后计算曲线曲率进行性别判决。
(4)采用面向对象思想,设计并开发基于人脸整体特征和基于下巴轮廓曲线的性别识别原型系统,并从实验角度验证了上述方法的有效性。
In recent years,With the further study of face recognition,gender recognition become the hotspot of computer vision and pattern recognition.Gender recognition is that use computer technology to identify human gender information by the face image.which have important application prospects in artificial intelligence、system monitor、pattern recognition and so on.
After analyzing the methods currently used by others,we present an Fusion algorithms of independent component analysis(ica) and Kernel Fisher Discriminant Analysis(KFDA)、an improved KNN-SVM algorithm for gender recognition and an curvature algorithm of chin profile curve.Works are described as below:
(1)A feature extraction algorithm which fuse ica algorithm and KFDA is proposed.Feature imformation which extracted by ICA algorithm can reconstruct more original data imformation and extracted by Fishe Discriminant Analysis can get more optimal classification information.Because of the influence of light、posture、expression,etc.so image data is nonlinear usually.but Fisher Discriminant Analysis only get linear information.therefore,we use KFDA algorithm and reduce time complexity of it by PCA.Images after preprocessing project to the subpace of ICA and KFDA.
(2)An improvement algorithm Improved KNN-SVM that combined Support Vector Machine(SVM) with weigthted K Nearest neighbour(KNN)is presented to improve the accuracy of gender recognition nearby SVM hyperplane.The algorithm gets optimal threshold by a few of known gender samples,then compute the distances from the test samples to the optimal superplane of SVM in feathure space,recognize gender after comparing the distance to threshold.
(3)A new Gender recognition method base on the curvature of face chin profile curve is presented.in order to get clear face chin profile curve.This method get the texture image by log-gabor algorithm firstly,then extract the feature of face chin profile curve through Active Appearance Model(AAM) moreover curve fitting by least squares.Get gender result by the curve's curvature lastly.
(4)A prototype system of face gender recognition based on holistic face feature and chin profile curve feature.It can be used to prove the effectiveness of above algorithms.
在综合分析国内外主要的特征提取方法和性别识别技术的基础上,本文提出基于独立成分分析和核Fisher判别分析相融合的特征提取算法、改进KNN-SVM的性别识别算法、基于人脸下巴轮廓曲线曲率的性别识别方法,并开发原型系统进行算法验证。具体内容如下:
(1)提出ICA和核Fisher判别分析(KFDA)相结合的特征提取方法。ICA得到的特征信息是更能重构原始数据的信息,Fisher判别分析则能得到相应的最佳分类的特征信息,由于光照、姿态、表情等变化引起人脸图像的差异造成人脸图像的分布往往是非线性的,而Fisher判别分析提取的是图像的线性特征,因此本文采用核Fisher判别分析(KFDA)算法,并用主成分分析(PCA)解决其存在的高时间复杂度问题,经过预处理的图像投影到ICA和KFDA张成的子空间中。
(2)提出加权KNN和SVM相结合的改进KNN-SVM性别识别算法。针对支持向量机(SVM)在超平面附近进行性别识别的不准确性,引入加权K近邻(KNN)算法,融合加权KNN和SVM进行性别识别。该算法用少量已知性别样本自动确定加权KNN与SVM的最优分类阈值,并计算待识别样本和支持向量机所确定的超平面的距离,通过距离与阈值的比较进行性别识别。
(3)提出基于人脸下巴轮廓曲线曲率的性别识别方法。该方法首先利用log-gabor对人脸进行处理,得到更能反映人脸纹理曲线的图像,然后利用AAM算法进行人脸下巴曲线定位,并对得到的人脸下巴特征点进行最小二乘拟合,最后计算曲线曲率进行性别判决。
(4)采用面向对象思想,设计并开发基于人脸整体特征和基于下巴轮廓曲线的性别识别原型系统,并从实验角度验证了上述方法的有效性。
In recent years,With the further study of face recognition,gender recognition become the hotspot of computer vision and pattern recognition.Gender recognition is that use computer technology to identify human gender information by the face image.which have important application prospects in artificial intelligence、system monitor、pattern recognition and so on.
After analyzing the methods currently used by others,we present an Fusion algorithms of independent component analysis(ica) and Kernel Fisher Discriminant Analysis(KFDA)、an improved KNN-SVM algorithm for gender recognition and an curvature algorithm of chin profile curve.Works are described as below:
(1)A feature extraction algorithm which fuse ica algorithm and KFDA is proposed.Feature imformation which extracted by ICA algorithm can reconstruct more original data imformation and extracted by Fishe Discriminant Analysis can get more optimal classification information.Because of the influence of light、posture、expression,etc.so image data is nonlinear usually.but Fisher Discriminant Analysis only get linear information.therefore,we use KFDA algorithm and reduce time complexity of it by PCA.Images after preprocessing project to the subpace of ICA and KFDA.
(2)An improvement algorithm Improved KNN-SVM that combined Support Vector Machine(SVM) with weigthted K Nearest neighbour(KNN)is presented to improve the accuracy of gender recognition nearby SVM hyperplane.The algorithm gets optimal threshold by a few of known gender samples,then compute the distances from the test samples to the optimal superplane of SVM in feathure space,recognize gender after comparing the distance to threshold.
(3)A new Gender recognition method base on the curvature of face chin profile curve is presented.in order to get clear face chin profile curve.This method get the texture image by log-gabor algorithm firstly,then extract the feature of face chin profile curve through Active Appearance Model(AAM) moreover curve fitting by least squares.Get gender result by the curve's curvature lastly.
(4)A prototype system of face gender recognition based on holistic face feature and chin profile curve feature.It can be used to prove the effectiveness of above algorithms.
引文
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[5]S.Yen,P.Sajda,and L.Finkel.Comparison of gender recognition by pdp and radial basis function networks.The Neurobiology of Computation,pages 433-438,1994.
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[8]B.Moghaddam and M.H.Yang.Learning gender with support faces.IEEE Transactions on PatternAnalysis and Machine Intelligence,24(5):707-711,2002.
[9]R.Iga,K.Izumi,H.Hayashi,G.Fukano,and T.Ohtani.A gender and age estimation system from face images.SICE 2003 Annual Conference,1,2003.
[10]刘文瑶,潘纲,吴朝晖,主元子空间中的SVM性别分类算法,第七届中国人工智能联合学术会议,桂林,广西,11.24-29,2002:133-137.
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[12]N.P.costen,M.Brown,and S.Akamatsu.Sparse models for gender classification.Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition,pages 201-206,2004.
[13]T.Wilhelm,H.J.Bohme,and H.M.Gross.Classification of Face Images for Gender,Age,Facial Expression,and Identity.Lecture notes in computer science,pages 569-574
[14]Saatci,Y.,and C.Town.Cascaded Classification of Gender and Facial Expression using Active Appearance Models.Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition.Pages:393-400.2006
[15]Bo WU,Hai zhou Al and Chang HUANG.Facial Image Retrieval Based on Demographic Classification,Proceedings of the17th International Conference on Pattern Recognition (ICPR'04)1051-4651/04
[16]BoWU,Hai zhou Al,Chang HUANG,Shi hong LAO,LUT-Based Adaboost for Gender Classifieation.
[17]孙宁,冀贞海,邹采荣,赵力.基于局部二元模式算子的人脸性别分类方法[J].华中科技大学学报(自然科学版),2007,(S1)
[18]Kawano,T.,Kato,K.;Yamamoto,K.An analysis of the gender and age differentiation using facial parts.Systems,Man and Cybernetics,2005 IEEE International Conference on.Volume:4,On page(s):3432-3436 Vol.4
[19]姚锡钢.基于肤色的人脸检测和性别识别的研究.(硕士论文)哈尔滨工业大学.2006年.
[20]A.Lapedriza,D.Masip,and J.Vitria.Are External Face Features Useful for Automatic Face Classification?.Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,.25 June 2005 Page(s):151-151
[21]K.Ueki,H.Komatsu,S.Imaizumi,K.Kaneko,N.Sekine,J.Katto,and T.Kobayashi.A method of gender classification by integrating facial,hairstyle,and clothing images.Pattern Recognition,2004.ICPR 2004.Proceedings of the 17th International Conference on Volume 4,23-26 Aug.2004 Page(s):446-449 Vol.4
[22]Phillips P J,Moon H,Rauss P J Rizvi s.The FERET evaluation methodology for face recognition algorithms.IEEE Trans.PAMI.page(s):1090-1104.2000,22(10)
[23]Viola.P,Jones.M.Robust real time object detection[A].8th IEEE International Conference on Computer Vision[C].Vancouver,2001 Page(s):87-87
[24]http://www.opencv.org.cn
[25]Bartlett,M.Stewart,Lades,H.Martin and Sejnowski,T.J.,Independent componentrepresentations for face recognition,Proceedings of the SPIE,Vol.3299:Conference on Human Vision and Electronic Imaging Ⅲ,pp.528-539,1998.
[26]Juneho Yi,Jongsun Kim,Jongmoo Choi,Junghyun Han,Eunseok Lee.Face Recognition Based on ICA Combined with FLD[A].Biometric Authentieation,2002.Pages:10-18
[27]Wei-min Liu,Chein-I Chang.Variants of Principal Components Analysis.2007 International Geoscience and Remote Sensing Symposium.2007.7.23:1083-1086.
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