基于旅客面部特征的实名制铁路车票检票身份认证算法

Identity Authentication Algorithm for Railway Real-Name Ticket Checking System Based on the Facial Features of Passengers

为了高效地完成实名制铁路车票的验票流程,提出1种基于旅客面部Gabor特征的身份认证算法。采用二维Gabor滤波器提取人脸图像的Gabor特征,并对这些特征进行变换和重组;对重组后得到的特征矩阵进行类内差和类间差运算,获得初始样本,并运用主成分分析法进行样本降维,构造出支持向量机分类器,从而通过支持向量机分类器实现旅客身份的认证。在F-Gnet人脸图像库上的实验结果表明,该算法的正确认证率可达94.14%,且对光照与人脸表情变化具有鲁棒性。

In order to efficiently complete the checking process for real-name train tickets,an identity authentication algorithm based on the facial Gabor features of passengers was proposed.Firstly,the two dimensional Gabor filters were used to extract the Gabor features of face images,and the features were transformed and reconstructed.Secondly,the intra-personal differences and the extra-personal differences of the reconstructed feature matrix were calculated to acquire the initial samples.The dimension reduction of the initial samples was conducted with the principal component analysis（PCA）,and then the support vector machine（SVM） classifiers were established.Finally,the identity authentication of passengers was completed by SVM classifiers.The experiments demonstrate that the proposed algorithm achieves 94.14% correct identity authentication accuracy on the F-Gnet,and has the robustness to the changes of illumination and expression.