面向人脸识别的WPD-HOG金字塔特征提取方法
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摘要
人脸识别技术可应用于各监控和安保领域,它涉及特征提取、识别模型等关键技术。其中特征提取方法直接影响识别效果,目前所用的特征提取方法存在特征表达不全面、计算复杂度高等问题。据此,提出一种基于WPDHOG金字塔的人脸特征提取方法,该方法结合小波包分解(Wavelet Packet Decomposition,WPD)、图像金字塔以及方向梯度直方图(Histograms of Oriented Gradients,HOG)对人脸图像特征进行有效表征,最终将WPD-HOG金字塔特征通过SVM分类器进行分类。通过在ORL人脸库上进行实验,与四种对比方法 HOG、HOG金字塔、FWPD-HOG以及FWPD-HOG金字塔进行比较,实验结果表明,WPD-HOG金字塔特征提取方法的识别率要高于对比方法,且在噪声方面具有较好的鲁棒性。
Face recognition technology can be applied in the field of monitoring and security, which involves key technologies such as feature extraction and recognition model. The feature extraction method has a direct influence on the recognition effect. At present, the feature extraction method has the problems of incomplete expression and high computational complexity. For solving this problem, this paper proposes a kind of facial feature extraction method:WPD-HOG pyramid.The WPD-HOG pyramid feature extraction method combines the Wavelet Packet Decomposition(WPD), image pyramid and Histograms of Oriented Gradients(HOG)together to characterize the face image feature. Finally, the WPD-HOG pyramid features are identified by the SVM classifier for face recognition. Experiments are conducted over ORL data set to demonstrate the proposed approach. Compared with the four benchmark methods:HOG, HOG pyramid, FWPD-HOG and FWPD-HOG pyramid, the experimental results show that the recognition performance, computation complexity and noise robustness of the proposed method are the best.
Face recognition technology can be applied in the field of monitoring and security, which involves key technologies such as feature extraction and recognition model. The feature extraction method has a direct influence on the recognition effect. At present, the feature extraction method has the problems of incomplete expression and high computational complexity. For solving this problem, this paper proposes a kind of facial feature extraction method:WPD-HOG pyramid.The WPD-HOG pyramid feature extraction method combines the Wavelet Packet Decomposition(WPD), image pyramid and Histograms of Oriented Gradients(HOG)together to characterize the face image feature. Finally, the WPD-HOG pyramid features are identified by the SVM classifier for face recognition. Experiments are conducted over ORL data set to demonstrate the proposed approach. Compared with the four benchmark methods:HOG, HOG pyramid, FWPD-HOG and FWPD-HOG pyramid, the experimental results show that the recognition performance, computation complexity and noise robustness of the proposed method are the best.
引文
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[15]许允之,方永丽,褚鑫,等.小波包频带-能量重构的异步电动机转子断条故障诊断[J].煤矿机电,2015,38(2):75-79.
[16]章毓晋.图像工程:上册,图像处理[M].北京:清华大学出版社,2006.
[2] Ghassabeh Y A,Rudzicz F,Moghaddam H A.Fast incremental LDA feature extraction[J].Pattern Recognition,2015,48(6):1999-2012.
[3] De A,Saha A,Pal M C.A human facial expression recognition model based on eigen face approach[J].Procedia Computer Science,2015,45:282-289.
[4] Jia M,Zhang Z,Song P,et al.Research of improved algorithm based on LBP for face recognition[J].Lecture Notes in Computer Science,2014,8833:111-119.
[5] Karthika R,Parameswaran L.Study of Gabor wavelet for face recognition invariant to pose and orientation[C]//Proceedings of the International Conference on Soft Computing Systems.[S.l.]:Springer India,2016.
[6] Déniz O,Bueno G,Salido J,et al.Face recognition using histograms of oriented gradients[J].Pattern Recognition Letters,2011,32(12):1598-1603.
[7] Tan H,Yang B,Ma Z.Face recognition based on the fusion of global and local HOG features of face images[J].IET Computer Vision,2014,8(3):224-234.
[8]万源,李欢欢,吴克风,等.LBP和HOG的分层特征融合的人脸识别[J].计算机辅助设计与图形学学报,2015(4):640-650.
[9]全雪峰.基于HOG-SVD特征的人脸识别[J].软件,2016,37(5):18-21.
[10]兰佩,方超.基于全局与局部特征融合的人脸识别方法[J].计算机与现代化,2014(3):109-113.
[11]杨冰,王小华,杨鑫,等.基于HOG金字塔人脸识别方法[J].浙江大学学报(工学版),2014,48(9):1564-1569.
[12]龚飞,金炜,符冉迪,等.融合小波包细节子图及稀疏表示的人脸识别[J].光电工程,2016,43(6):32-38.
[13]王文剑,门昌骞.支持向量机建模及应用[M].北京:科学出版社,2014.
[14] Dalal N,Triggs B.Histograms of oriented gradients for human detection[C]//IEEE Computer Society Conference on Computer Vision&Pattern Recognition,2005:886-893.
[15]许允之,方永丽,褚鑫,等.小波包频带-能量重构的异步电动机转子断条故障诊断[J].煤矿机电,2015,38(2):75-79.
[16]章毓晋.图像工程:上册,图像处理[M].北京:清华大学出版社,2006.