最小二乘支持向量机在人脸识别中的应用

 支持向量机(SVM)模式识别方法具备良好的分类性能和鲁棒性,在介绍了典型支持向量机与最小二乘支持向量机(LS_SVM)原理的基础上,给出最小二乘支持向量机的算法实现过程,将其应用于人脸识别当中,取得较典型支持向量机在时间上较好的效果.在OPL人脸库中的实验结果表明,基于LS_SVM的人脸自动识别系统更能适用于实时性要求较高的场合.

The application of least squares Support Vector Machine in face recognition

Support Vector Machine (SVM)is a popular discriminant method for the very purpose of achieving high separability between the different patterns in whose classification one is interested with good classification and robust performance.The method of calculation of Least Squares Support Vector Machine (LS_SVM) is introduced on the basis of the classic SVM and LS_SVM theories and used in face recognition to achieve a faster effect than the typical SVM.The experimental results on ORL databases show that LS_SVM system is more applicable to the face recognition environment which demanding realtime application.