01555nas a2200181   4500008004100000245008300041210006900124260000900193300001400202490000700216520090000223653002401123653002101147653004001168653003301208100001501241856011701256       2013                        eng d00aDigital information facial recognition based on PCA and its improved algorithm0 aDigital information facial recognition based on PCA and its impr  c2013  a390 - 3950 v113 aFace recognition (PCA algorithm) based on principal component analysis method is a classic algorithm of digital information face recognition. This paper first analyzes the PCA algorithm and its basic principles. And in view of the shortage that it is affected by its subject facial expression changes and other factors, it designs the improved PCA algorithm, namely adding the image enhancement processing method, such as the local mean and standard deviation, to the original algorithm to increase the robustness of human face illumination and facial expression change during recognition. In addition, verify it on ORL face database and the results show that the improved PCA algorithm can get a higher resolution of the human face and up to 81.59% accuracy rate. The average growth of time-consuming is 0.378 seconds compared to traditional algorithms, and it is a fruitful improved algorithm.10aDigital information10aFace recognition10aImage enhancement processing method10aPrincipal component analysis1 aZhu, H.-F.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84890454789&partnerID=40&md5=9180e88599a60f794e63948a9a7036db01531nas a2200193   4500008004100000245008300041210006900124260000900193300001400202490000700216520086100223653002801084653001601112653002901128653003301157653001901190100001101209856011701220       2012                        eng d00aA novel method of data stream classification based on incremental storage tree0 anovel method of data stream classification based on incremental   c2012  a301 - 3070 v103 aFor the characteristics of large number, fast change, high cost of random access of data stream, this paper proposes a Bayesian classification data mining algorithm based on incremental storage tree to handle the problems. Use sliding window to process data stream and divide it into several basic units, apply Principal component analysis (PCA) to compress the data from window and produce dynamic incremental storage tree, use the second power strategy to continuously update several incremental storage tree. Then use multi-classifier integration technology combines with Bayesian classification to produce Bayesian classifier. At last, adjust the weight of classifier by cyclic test, and get high classification accuracy. Detailed simulation analysis demonstrates that the presented algorithm is of high efficiency of space and time and is more stable.10aBayesian classification10aData stream10aIncremental storage tree10aPrincipal component analysis10aSliding window1 aXu, C.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870803724&partnerID=40&md5=ee151686cdc13a9a1a785eb65f87d46701211nas a2200193   4500008004100000245007200041210006900113260000900182300001400191490000700205520054100212653002100753653002300774653003300797653004100830100001600871700001300887856011700900       2012                        eng d00aResearch on face recognition based on wavelet transform and PCA-ANN0 aResearch on face recognition based on wavelet transform and PCAA  c2012  a136 - 1410 v103 aThis paper mainly studies the basic theory and technology on the identification of people's faces. The better methods are chosen and a new method is proposed. It integrates three elements (Wavelet Transform, Principal Component Analysis, Artificial Neural Networks), and the method can recognize faces more effectively. Meanwhile, this method lessens the effect of light on face appearance. Experimental result indicates that our new face recognition system is effective in the aspects of both correct ratio and speed of distinguishing.10aFace recognition10aFeature extraction10aPrincipal component analysis10aRadial basis function neural network1 aYuanzhi, W.1 aXiao, L.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866450615&partnerID=40&md5=f0bb715fcc45b0395a7a2fee9540fdb9