【摘要】 对基于主元分析(PCA)、二维主元分析(2DPCA)和Fisher线性判别分析(FLDA)的人脸识别方法进行了比较研究.在分析了每一种方法的特点、应用场合和限制条件的基础上,提出了基于2DPCA和FLDA相结合的人脸识别算法.最后,利用AT&T人脸库,对基于这几种算法以及它们的组合算法的人脸识别方法进行了仿真比较实验,结果表明基于2DPCA的人脸识别方法识别精度高于基于PCA的方法;基于2DPCA和FLDA的人脸识别方法在保证分类精度的条件下提高了分类的速度,具有较好的识别性能.更多还原

【Abstract】 Some of face recognition methods based on Principal Component Analysis(PCA),Two-dimensional Principal Component Analysis(2DPCA) and Fisher’s Linear Discriminant Analysis(FLDA) are comparatively studied in this paper.On the basis of the analysis of characteristics,application occasion and limitations,a new method of face recognition based on combining 2DPCA and FLDA is proposed.Finally,comparison simulations are performed with the methods and their combined methods by means of AT&T face database.The results show that the methods based on 2DPCA can have better recognition precision than the ones based on PCA;the method based on 2DPCA and FLDA improves the real-time characteristics in the condition of keeping the recognition precision and has the best performance.更多还原