基于深度模型的属性学习在人脸验证中的应用研究
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摘要
人脸属性,如性别,年龄等对于特征人脸的构成具有唯一性。针对传统人脸验证方法的研究,提出了一种基于深度模型的属性预测方法。该方法是基于深度卷积神经网络模型提取的人脸特征表示,通过标记属性信息的数据训练分类器进行属性预测,并将其用于人脸验证环节以提高验证准确率。该方法提供了一种从深度模型提取的人脸特征表示中分析人脸属性的思路,实验证明,该方法在实际应用中能够有效提高人脸验证的准确率。
Face attributes,such as gender and age,are unique to feature faces.Aiming at the research of traditional face verification,we proposed a depth model based on attribute prediction method.The method is based on the deep convolution neural network model to extract the facial feature representation,and the classifier is trained by the data of labeled attribute information to predict the attributes.And we use it in face verification.This method provides a method for analyzing face attributes from facial fea-tures extracted from depth model.Experiments show that this method can effectively improve the accuracy of face verification in practical applications.
Face attributes,such as gender and age,are unique to feature faces.Aiming at the research of traditional face verification,we proposed a depth model based on attribute prediction method.The method is based on the deep convolution neural network model to extract the facial feature representation,and the classifier is trained by the data of labeled attribute information to predict the attributes.And we use it in face verification.This method provides a method for analyzing face attributes from facial fea-tures extracted from depth model.Experiments show that this method can effectively improve the accuracy of face verification in practical applications.
引文
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[9] JOACHIMS T.Making large-scale SVM learning practical[C]//Technische Universit-t Dortmund,Sonderforschungsbereich475:Komplexit?tsreduktion in multivariaten Datenstrukturen,1998:499—526.
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[11]PHILLIPS P J,MOON H,RAUSS P,et al.The FERET september1996 database and evaluation procedure[J].Lecture Notes in Computer Science,1997,1206:395—402.
[12]韩萌,丁剑.基于交叉验证的BP算法的改进与实现[J].计算机工程与设计,2008,29(14):3738—3739.
[13]邵蔚元,郭跃飞.多任务学习及卷积神经网络在人脸识别中的应用[J].计算机工程与应用,2016,52(13):32—37.
[2] KUMAR N,BERG A C,BELHUMEUR P N,et al.Attribute and simile classifiers for face verification[C]//IEEE,International Conference on Computer Vision.IEEE,2010:365—372.
[3] ZHANG N,PALURI M,RANZATO M,et al. PANDA:pose aligned networks for deep attribute modeling[J].Computer Vision and Pattern Recognition,2016:1637—1644.
[4] BOURDEV L,MAJI S,MALIK J.Describing people:a poseletbased approach to attribute classification[C]//IEEE International Conference on Computer Vision.IEEE,2011:1543—1550.
[5] BERG T,BELHUMEUR P N.Tom-vs-Pete classifiers and identity-preserving alignment for face verification[C]//BMVC.2012,2:7.
[6] CAO X,WIPF D,WEN F,et al.A practical transfer learning algorithm for face verification[C]//Proceedings of the IEEE International Conference on Computer Vision,2013:3208—3215.
[7] LI H,LIN Z,SHEN X,et al.A convolutional neural network cascade for face detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015:5325—5334.
[8]段向军.基于粒子群算法和神经网络的人脸识别分类器研究[J].计算技术与自动化,2011,30(2):115—117.
[9] JOACHIMS T.Making large-scale SVM learning practical[C]//Technische Universit-t Dortmund,Sonderforschungsbereich475:Komplexit?tsreduktion in multivariaten Datenstrukturen,1998:499—526.
[10]李彦冬,郝宗波,雷航.卷积神经网络研究综述[J].计算机应用,2016,36(9):2508—2515.
[11]PHILLIPS P J,MOON H,RAUSS P,et al.The FERET september1996 database and evaluation procedure[J].Lecture Notes in Computer Science,1997,1206:395—402.
[12]韩萌,丁剑.基于交叉验证的BP算法的改进与实现[J].计算机工程与设计,2008,29(14):3738—3739.
[13]邵蔚元,郭跃飞.多任务学习及卷积神经网络在人脸识别中的应用[J].计算机工程与应用,2016,52(13):32—37.