人脸识别外文文献.doc
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1、Method of Face Recognition Based on RedBlack Wavelet Transform and PCAYuqing He, Huan He, and Hongying YangDepartment of Opto-Electronic Engineering,Beijing Institute of Technology, Beijing, P。R。 China, 100081 20701170bit。edu。cnAbstract。 With the development of the manmachine interface and the recog
2、ni-tion technology, face recognition has became one of the most important research aspects in the biological features recognition domain. Nowadays, PCA(Principal Components Analysis) has applied in recognition based on many face database and achieved good results。 However, PCA has its limitations: t
3、he large volume of computing and the low distinction ability. In view of these limitations, this paper puts forward a face recognition method based on redblack wavelet transform and PCA. The improved histogram equalization is used to realize image preprocessing in order to compensate the illuminatio
4、n。 Then, appling the red-black wavelet sub-band which contains the information of the original image to extract the feature and do matching。 Comparing with the traditional methods, this one has better recognition rate and can reduce the computational complexity.Keywords: Red-black wavelet transform,
5、 PCA, Face recognition, Improved histogram equalization。1 IntroductionBecause the traditional status recognition (ID card, password, etc) has some defects, the recognition technology based on biological features has become the focus of the research。 Compared with the other biological features (such
6、as fingerprints, DNA, palm prints, etc) recognition technology, people identify with the people around mostly using the biological characteristics of human face. Face is the most universal mode in human vision。 The visual information reflected by human face in the exchange and contact of people has
7、an important role and significance. Therefore, face recognition is the easiest way to be accepted in the identification field and becomes one of most potential iden-tification authentication methods. Face recognition technology has the characteristics of convenient access, rich information. It has w
8、ide range of applications such as identification, drivers license and passport check, banking and customs control system, and other fields1。The main methods of face recognition technology can be summed up to three kinds: based on geometric features, template and model separately。 The PCA face recogn
9、ition method based on KL transform has been concerned since the 1990s。 It is simple, fast. and easy to use。 It can reflect the person faces characteristic on the whole. Therefore, applying PCA method in the face recognition is unceasingly improving.D.-S。 Huang et al. (Eds。): ICIC 2008, LNCS 5226, pp
10、. 561568, 2008. Springer-Verlag Berlin Heidelberg 2008This paper puts forward a method of face recognition based on RedBlack wavelet transform and PCA。 Firstly, using the improved image histogram equalization2 to do image preprocessing, eliminating the impact of the differences in light intensity。 S
11、econdly, using the Red-Black wavelet transform to withdraw the blue subband of the relative stable face image to obscure the impacts of expressions and postures. Then, using PCA to withdraw the feature component and do recognition。 Comparing with the traditional PCA methods, this one can obviously r
12、educe computational complexity and increase the recognition rate and anti-noise performance. The experimental results show that this method mentioned in this paper is more accurate and effective.2 RedBlack Wavelet TransformLifting wavelet transform is an effective wavelet transform which developed r
13、apidly these years。 It discards the complex mathematical concepts and the telescopic and translation of the Fourier transform analysis in the classical wavelet transform。 It de-velops from the thought of the classical wavelet transform multi-resolution analysis. Red-black wavelet transform34 is a tw
14、odimensional lifting wavelet transform56, it contains horizontal/vertical lifing and diagonal lifting. The specific principles are as bellow。2.1 Horizontal /Vertical LiftingAs Fig。1 shows, horizontal /vertical lifting is divided into three steps:1。 Decomposition: The original image by horizontal and
15、 vertical direction is divided into red and black block in a crossblock way。2. Prediction: Carry on the prediction using horizontal and the vertical direction four neighborhoods red blocks to obtain a black block predicted value。 Then, using the difference of the black block actual value and the pre
16、dicted value to substitute the black block actual value. Its result obtains the original image wavelet coefficient。 As Fig。1(b) shows: (1)3。 Revision:Using the horizontal and vertical direction four neighborhoods black blocks wavelet coefficient to revise the red block actual value to obtain the app
17、roximate signal. As Fig.1(c) shows: (2)In this way, the red block corresponds to the approximating information of the image, and the black block corresponds to the details of the image。2.2 Diagonal LiftingOn the basis of horizontal /vertical lifting, we do the diagonal lifting。 As Fig.2 shows, it is
18、 also divided into three steps: Fig.2。Diagonal lifting1.Decomposition: After horizontal /vertical lifting, dividing the obtained red block into the blue block and the yellow block in the diagonal cross way。 2. Prediction: Using four opposite angle neighborhoods blue block to predict a data in order
19、to obtain the yellow block predicted value. Then the difference of the yellow block actual value and the predicted value substitutes the yellow block actual value. Its result obtains the original image wavelet coefficient of the diagonal direction。 As Fig.2(b) shows: (3)3。 Revision: Using four oppos
20、ite angle neighborhood yellow block wavelet coefficient to revise the blue block actual value in order to obtain the approximate signal。 As Fig。2(c) shows: (4)After the second lifting, the redblack wavelet transform is realized.According to the Equations, it can analyze some corresponding relations
21、between the red-black wavelet transform and the classical wavelet transform: namely, the blue block is equal to the subband LL of the classical tensor product wavelets, the yellow block is equal to sub-band HH and the black block is equal to sub-band HL and LH. Experimental results show that it disc
22、ards the complex mathematical concepts and equations。 The relativity of image can mostly be eliminated and the sparser representation of image can be obtained by the Red-Black wavelet transform。The image after Red-Black wavelet transform is showed in the Fig.3(b), on the left corner is the blue sub-
23、band block image which is the approximate image of original image。Fig.3.The result of red-black wavelet transform3 Feature Extraction Based on PCA7PCA is a method which analyses data in statistical way. This method discovers group of vectors in the data space。 Using these vectors to express the data
24、 variance as far as possible. Putting the data from the P-dimensional space down to Mdimensional space ( PM)。 PCA use KL transform to obtain the minimumdimensional image recognition space of the approximating image space. It views the face image as a highdimensional vector. The high-dimensional vect
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