基于集成人脸对距离学习的跨年龄人脸验证
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摘要
针对不同年龄跨度下人脸对差异的不同,文中提出基于集成人脸对距离学习(EFPML)的跨年龄人脸验证方法.对不同年龄跨度的人脸对分别学习距离度量,然后使用集成方法对人脸对进行重表示,使人脸对重表示更具有判别性,并且可以扩充有限的跨年龄数据集.在公开的跨年龄人脸数据库FG-NET和CACD上的实验表明,文中方法可以有效减少年龄带来的影响,提高验证性能.
Aiming at the variations of face pairs caused by different age gaps,an ensemble face pairs distance metric learning method(EFPML) is proposed for cross-age face verification. Firstly,the whole dataset is divided into several subsets with different age gaps. Then,a distance metric is learned for each subset.Finally,all face pairs are re-represented for many times via learnt distance metrics, the new representations are more distinguishable and the limited cross-age face data are expanded. To evaluate the proposed method,a series of experiments are conducted on two real-world cross age datasets,FG-NET and CACD. The results show that EFPML consistently outperforms the state-of-the-art methods and it has ability to reduce the effect of aging and improve verification performance.
Aiming at the variations of face pairs caused by different age gaps,an ensemble face pairs distance metric learning method(EFPML) is proposed for cross-age face verification. Firstly,the whole dataset is divided into several subsets with different age gaps. Then,a distance metric is learned for each subset.Finally,all face pairs are re-represented for many times via learnt distance metrics, the new representations are more distinguishable and the limited cross-age face data are expanded. To evaluate the proposed method,a series of experiments are conducted on two real-world cross age datasets,FG-NET and CACD. The results show that EFPML consistently outperforms the state-of-the-art methods and it has ability to reduce the effect of aging and improve verification performance.
引文
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[4]DING C X,XU C,TAO D C.Multi-task Pose-Invariant Face Recognition.IEEE Transactions on Image Processing,2015,24(3):980-993.
[5]ZHU X Y,LEI Z,YAN J J,et al.High-Fidelity Pose and Expression Normalization for Face Recognition in the Wild//Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2015:787-796.
[6]RAMANATHAN N,CHELLAPPA R.Modeling Age Progression in Young Faces//Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2006,I:387-394.
[7]DU L,LING H B.Cross-Age Face Verification by Coordinating with Cross-Face Age Verification//Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2015:2329-2338.
[8]RAMANATHAN N,CHELLAPPA R.Face Verification across Age Progression.IEEE Transactions on Image Processing,2006,15(11):3349-3361.
[9]LANITIS A.A Survey of the Effects of Aging on Biometric Identity Verification.International Journal of Biometrics,2009,2(1):34-52.
[10]JAIN A K,TONG Y,PARK U.Age-Invariant Face Recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(5):947-954.
[11]LANITIS A,TAYLOR C J,COOTES T F.Toward Automatic Simulation of Aging Effects on Face Images.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(4):442-455.
[12]SHU X B,TANG J H,LAI H J,et al.Personalized Age Progression with Aging Dictionary//Proc of the IEEE International Conference on Computer Vision.Washington,USA:IEEE,2015:3970-3978.
[13]LING H B,SOATTO S,RAMANATHAN N,et al.Face Verification across Age Progression Using Discriminative Methods.IEEE Transactions on Information Forensics and Security,2010,5(1):82-91.
[14]BERETA M,KARCZMAREK P,PEDRYCZ W,et al.Local Descriptors in Application to the Aging Problem in Face Recognition.Pattern Recognition,2013,46(10):2634-2646.
[15]ALI A S O,SAGAYAN V,SAEED A M,et al.Age-Invariant Face Recognition System Using Combined Shape and Texture Features.IET Biometrics,2015,4(2):98-115.
[16]LU J W,ZHOU X Z,TAN Y P,et al.Neighborhood Repulsed Metric Learning for Kinship Verification.IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(2):331-345.
[17]HINTON G E,SALAKHUTDINOV R R.Reducing the Dimensionality of Data with Neural Networks.Science,2006,313(5786):504-507.
[18]WANG W,CUI Z,YAN Y,et al.Recurrent Face Aging//Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2016:2378-2386.
[19]LIN L,WANG G R,ZUO W M,et al.Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning.IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1089-1102.
[20]WEN Y D,LI Z F,QIAO Y.Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition//Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2016:4893-4901.
[21]ZHOU Z H.Ensemble Methods:Foundations and Algorithms.Boca Raton,USA:CRC Press,2012.
[22]CHEN B C,CHEN C S,HSU W H.Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval//Proc of the European Conference on Computer Vision.Berlin,Germany:Springer,2014:768-783.
[23]AHONEN T,HADID A,PIETIKAINEN M.Face Description with Local Binary Patterns:Application to Face Recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(12):2037-2041.
[24]LOWE D G.Distinctive Image Features from Scale-Invariant Keypoints.International Journal of Computer Vision,2004,60(2):91-110.