面向人脸识别的WPD-HOG金字塔特征提取方法  

WPD-HOG pyramid feature extraction method for face recognition

作　　者：刘文培 李凤莲[1] 张雪英[1] 田玉楚[1,2] LIU Wenpei;LI Fenglian;ZHANG Xueying;TIAN Yuchu(College of Information Engineering,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China;School of Electrical Engineering and Computer Science,Queensland University of Technology,Queensland,Australia)

机构地区：[1]太原理工大学信息工程学院,山西晋中030600 [2]昆士兰科技大学电机工程及计算机科学学院

出　　处：《计算机工程与应用》2018年第22期150-155,共6页Computer Engineering and Applications

基　　金：山西省国际合作项目(No.2015081007);2016年太原理工大学教改项目(No.24);山西省优秀人才科技创新项目(No.201605D211021)

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.

关 键 词：人脸识别特征提取 小波包分解 图像金字塔 方向梯度直方图 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]