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

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.