@article {416,
	title = {Digital information facial recognition based on PCA and its improved algorithm},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {390 - 395},
	abstract = {Face 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.},
	keywords = {Digital information, Face recognition, Image enhancement processing method, Principal component analysis},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84890454789\&partnerID=40\&md5=9180e88599a60f794e63948a9a7036db},
	author = {Zhu, H.-F.}
}
@article {345,
	title = {A novel method of data stream classification based on incremental storage tree},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {301 - 307},
	abstract = {For 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.},
	keywords = {Bayesian classification, Data stream, Incremental storage tree, Principal component analysis, Sliding window},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870803724\&partnerID=40\&md5=ee151686cdc13a9a1a785eb65f87d467},
	author = {Xu, C.}
}
@article {316,
	title = {Research on face recognition based on wavelet transform and PCA-ANN},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {136 - 141},
	abstract = {This paper mainly studies the basic theory and technology on the identification of people{\textquoteright}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.},
	keywords = {Face recognition, Feature extraction, Principal component analysis, Radial basis function neural network},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866450615\&partnerID=40\&md5=f0bb715fcc45b0395a7a2fee9540fdb9},
	author = {Yuanzhi, W. and Xiao, L.}
}
