人脸识别外文文献

1、Method of Face Recognition Based on Red-BlackWavelet Transform and PCAYuqing He, Huan He, and Hongying Yang Department of Opto-Electronic Engineering, Beijing Institute of Technology, Beijing, P.R. China, 100081Abstract. With the development of the man-machine interface and the recogni-tion technolo

2、gy, 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: the large volume

3、of computing and the low distinction ability. In view of these limitations, this paper puts forward a face recognition method based on red-black wavelet transform and PCA. The improved histogram equalization is used to realize image pre-processing in order to compensate the illumination. Then, appli

4、ng 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, PCA, Face rec

5、ognition, Improved histogram equalization.1 IntroductionBecause the traditional status recognition (ID card, password, etc) has somedefects, the recognition technology based on biological features has becomethe focus of the re-search. Comparedwith the other biological features (such as fingerprints,

6、 DNA, palm prints, etc) recognition technology, people identify with the people around mostly using the biological characteristics of humanface. Face is the most universal mode in human vision. The visual information reflected by human face in the exchange and contact of people has an important role

7、 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 wide range of appl

8、ications such as iden-tification, driver's license and passport check, banking and customs control system, and other fields1.The main methods of face recognition technology can be summed up tothree kin ds: based on geometric features, template and model separately. The PCAace recognition method

9、based on K-L transform has been concerned since the 1990s. It is simple, fast. and easy to use. It can reflect the person face's characteristic on the whole. Therefore, applying PCAmethod in the face recog niti on is un ceas in gly impro ving.D.-S. Hua ng et al. (Eds.): ICIC 2021, LNCS5226, pp.

10、561 - 568, 2021. ? Springer -Verlag Berlin Heidelberg 2021This paper puts forward a method of face recognitionbased onRed-Black wavelet tran sform and PCA. Firstly, using the improved image histogram equalization2 to do image preprocessing, eliminating the impact of the differences in light intensit

11、y. Sec-ondly, using the Red-Black wavelet transform to withdraw the blue sub-band of the relative stable face image to obscure the impacts of expressions and postures. Then, using PCAto withdraw the feature component and do recognition.Comparingwith the traditionalPCA methods, this one can obviously

12、 reducecomputati onal complexity and in crease the recog niti onrate and an ti-no iseperformanee. The experimental results show that this method mentioned in this paper is more accurate and effective.2 Red-Black Wavelet Tran sformLifti ng wavelet tran sform is an effective wavelet tran sform which d

13、eveloped rapidly these years. It discards the complex mathematical con cepts and the telescopic and tran slati on of the Fourier tran sform analysis in the classical wavelet transform. It de-velops from the thought of the classical wavelet transform multi-resolution analysis. Red-black wavelet tran

14、sform3-4 isatwo-dime nsionallifting wavelettransform5-6,it containshorizontal/verticallifing and diagonallifting. The specific principles are as bellow.U)(cjHg, Lhcfizontil /vertical lifting2.1Horiz on tal /Vertical Lifti ngAs Fig.1 shows, horiz on tai /vertical lift ing is divided in to three steps

15、:1. Decompositi on: The orig inal image by horiz on tal and verticaldirecti on is divided into red and black block ina cross-block way.2. Prediction:Carry on the predictionusing horizontaland thevertical direct ion four n eighborhood's redblocks to obta in a black block predicted value.Then, usi

16、ng the differe nee of the black block actual value and the predicted value to substitute the black block actual value. Its result obta ins the orig inal image wavelet coefficie nt. As Fig.1(b) shows:f(i,j) f(i,j) f(i 1,j) f(i,j 1) f(i, j 1) f(i 1,j) /4(i mod 2 j mod 2) 13. Revisi on:Using the horiz

17、on tal and vertical directi on four n eighborhood's black block's wavelet coefficient to revise the red block actual value to obtain the approximate sig nal. As Fig.1(c) shows:f(i,j) f(i,j) f(i 1,j) f(i,j 1) f(i, j 1) f(i 1,j) /8(i mod 2 j mod 2)2In this way, the red block corresp onds to th

18、e approximat ing information of the image, and the black block corresponds to the details of the image.2.2Diag onal Lifti ngOn the basis of horiz on tal /vertical lift ing, we do the diago nallifti ng. As Fig.2 shows, it is also divided in to three steps:Fig.2. Diag on al lift ing1.Decomposition: Af

19、ter horizontal /vertical lifting, dividing the obta ined red block into the blue block and the yellow block in thediago nal cross way.2. Predict ion:Using four opposite an gle n eighborhood's blueblock to predict a data in order to obtain the yellow block predicted value. Then the differe nee of

20、 the yellow block actual value and the predicted value substitutes the yellow block actual value. Its resultobta ins the orig inal image wavelet coefficie ntof the diag onal directi on.As Fig.2(b) shows:f(i,j) f(i,j) f(i 1, j 1) f(i 1, j 1) f(i 1, j 1) f(i 1, j 1) 14(i mod 21, j mod 21) 33. Revision

21、:Usingfour opposite angle neighborhood yellowblock wavelet co-efficientto revise the blue block actual value in orderto obtai n the approximate sig nal. As Fig.2(c) shows:f(i,j) f(i,j) f(i 1, j 1) f(i 1, j 1) f(i 1, j 1) f(i 1, j 1) /8(i mod 20, j mod 20)4After the sec ond lifti ng, the red-black wa

22、velet tran sform is realized.Accord ing to the Equati on s, it can an alyze some corresp onding relati ons betwee n the red-black wavelet tran sform and the classical wavelet tran sform: n amely,the blue block is equal to the sub-ba nd LL ofthe classical ten sor product wavelets, the yellow block is

23、 equal to sub-ba nd HH and the black block is equal to sub-ba nd HL and LH.Experime ntal results show that it discards the complex mathematical concepts and equations. The relativity of image can mostly be eliminated and the sparser represen-tationof image can be obtained by the Red-Blackwavelet tra

24、n sform.The image after Red-Black wavelet transform is showed in the Fig.3(b), on the left corner is the blue sub-ba nd block image which is the approximate image of orig inal image.Fig.3. The result of red-black wavelet transform3 Feature Extraction Based on PCA7PCA is a method which analyses data

25、in statistical way. This method discovers group of vectors in the data space. Using these vectors to express the data variance as far as possible. Putting the data from the P-dimensional space down to M-dimensional space ( P»M). PCA use K-L tran sform to obta in the minimu m-dime nsio nal image

26、 recog ni-tio n space of the approximating image space. It views the face image as a high-dimensional vector. The high-dimensional vector is composedof each pixel. Then thehigh-dime nsionalin formati onspace maps thelow-dime nsional characteristic subspace by K-L tran sform. It obta ins a group of o

27、rthogo nal bases through high-dime nsional face image space K-L transform. The partial retention of orthogonal bases creates the low-dimensional subspace. The orthogonal bases reserved is called “ Principle component" . Since the image corresponding to the orthogonal bases just like face, so th

28、is is also called“Eigenfaces " method. Thearithmetic of feature extraction are specified as follows:For a face image of m x n, conn ect ing its each row will con stitute a row vector which has D= m x n dimen sions. The D is the face image dimensions. Supposing Mis the number of training samples

29、, x j is the face image vector which is derived from the jth picture, so the covaria nce matrix of the whole samples is:Msr(Xj U)(Xj u)T 5j 1And the 卩 is the average image vector of the training samples:1 M u xj 6M j i jOrderi ngAx1u, x2u,xMu ,soSpAATa ndits demisio nis D D.(7)Accord ing to the prin

30、 cipleof K-L tran sform, the coord in ate weachieved is com-posed of eige nvector corresp onding to non zero eige nv alue of matrix 八川 Computi ng out the eige nvalue and Orthogo nal no rmalized vector of matrix D x D directly is diffi -cult. So according to the SVD principle, it can figure out the e

31、igenvalue and eigen-vector of matrixthrough gett ing the eige nv alue and eige nvector of matrixi (i 1,2,，r) is r non zero eige nvalue of matrixvi :is theeige nv ectorcorre-sp ondingto i so the orthogo nalno rmalized7"i eigenvector of matrix 尺* is as bel-low:This is the eige nv ector of心、Arrang

32、ing its eigenvalues accordingto the size:12 i ,its corresponding eigenvector is i. In this way, each face image can project on the sub-space composed of,the former dIn order to reduce the dimension,it can selecteigenvectors as sub-space. It can select d biggest eigenvectors according to the en ergy

33、proporti on which the eige nvalue occupies:£扎/£扎工虫(8)Usually ordering a =90%99%.As a result , the image corresponding to these eigenvectors are similar to the humanface, it is also called “ Eigenfaces " .So the method which uses PCA transform is called “Eigenfaces " method. Owing

34、 to the Drop- dimensionalspace composed of “ Ei -genfaces " , each image canproject on it and get a group of coordinate coefficients which shows the location of the sub-space of this image, so it can be used as the bases for face recognition.Therefore, it can use the easiest Nearest NeighborCla

35、ssifier8 to classify the faces.4 Experime nts and ResultsThis part mainly verifies the feasibility and superiority of the algorithm through the comparis on of the experime ntal data.4.1 Experime ntal Con diti ons and ParametersSelect ingImages AfterDelam in ati on.After Red-Black wavelettran sform,

36、we only select the data of the blue block, because the part of the blue block represe nts the ap-proximat ing face image and it is not sensitive to the expression and illumination and even filtrates the image n oise.The In flue nee of The Blue Block En ergy Caused By Decompositi on Layers. In the ex

37、periment data9 of the Red-Black transform, we can find that it does not have the energy centralized characteristicunder the situationof multi-layer decomposition. As the Table 1 shows that different layer decompositions obtain different energies. Test image is the internationalstandard image Lena512

38、, its energy is 4.63431e+009 and entropy is 7.4455.Table L.Ited-Elack wave lei energy? decomprtsitiotchartLayersI2345Low-paAA energyt. %)(1 4780.7719CL 43320. 153STotal energy1.17e+00g3.09e+(M)SThe orig inal image en ergy wastage is due to the black and yellow block tran sform.According to the forme

39、r results and the size of the face image (112 x 92), one layer decompositi on can be done to achieve satisfactory results. The blue block sub-band not only has no incentive to expression and gestures, but also retains the differenee of differentfaces. At thesame time it reduces the image vector dime

40、 nsions and the com-plexity of the algorithm. If the size of the originalimage is bigger and theresolution is higher, the multi-layer decompositions can be considered.Database Selectio n. We choose the public ORL database to do some relatedex- periments.This database contains 40 differentpeople'

41、 simages which are captured in different periods and situation. It has 10 pictures per person and 400 pictures in all. The background is black. Each picture has 256 grayscales and the size is 112x 92. The face images inthe database have the differe nt facial expressi ons and the differe nt facial de

42、tail cha nges. The facial postures also have the cha nges. At prese nt, this is the most exte nsive face database.4.2 Experime nt Processes and ResultsThe method of face recog niti on based on Red-Black wavelet tran sform and PCA shows as bellow: Firstly, using the improved image histogram equalizat

43、i on to do pretreatme nt, elim in at ing the impact of the differe nces in light in ten sity. Sec on dly, using the Red-black wavelet transform to withdraw the blue block sub-band of the relative stable person face image achieved the effects of obscuring impacts of expressi ons and pos-tures. Then,

44、using PCA to withdraw the feature comp onent and do recog niti on. We adopt 40 pers on and 5 pictures per person whentraining, so there are 200 pictures as the training samples all together. Then carrying on recognition to the other 200 pictures under the con diti ons of whether there are illu min a

45、ti on and Red-Black waveletFig. 5-Orig.inaJ face iinagciFig. 6”of EllLumnadonresultsFig, 7- rtnage i)f one tsycrRud- liitick wsiveJut 址皿耳- forni resultstran s-form or not.Image Preprocessing. First using the illumination compensation on the original face images (Fig.5) in the database, namely doing

46、gray adjustment and normalization, the images (Fig.6) after transform are obviously clearer tha n the former ones and helpful for an alysis and recog niti on.Put the one layer Red-Black wavelet transform on the com-pensation images, then withdraw the images of the blue block sub-band which are the l

47、ow-dimensionapproximatingimages of the original images (Fig.7).Appling Red-Black wavelet transform on the images plays an important role in obscuri ng the impacts of face expressi ons and postures and achieved good effects of reduci ng dime nsions.Tahle 2. Face, reeogniiirtn precision tinie on d.iff

48、t?rent TTKidelsPCA+R-ed'Black PCA+ illutninaiion FCA+ il luminal ion compensationWiivelei iranxfbrmcoinpensdLionRed-Black winriiloimReeogniiion rates75*S5%93%Traiining10IfiRecogn iMn iine(s)0.210.28Feature Extractio n and Matci ng Results. After the image preprocess in g,we adopt PCA to extract

49、features and recog ni ze. This paper an alyses the results of three models which separately are PCAcombined with Red-Black wavelet tran sform,illu min ati oncompe nsati on,Red-Black wavelettransform and illuminationcompensation. The recognitionrates, trainingand recognition time of different models

50、are showed in the Table 2. We can see that withdraw ing the blue block sub-ba nd can obviously reduces the dimensions of the image vector and computation. The reducing training time shows that the low resoluti on subgraph reduces the computati onal complexity of the PCA through the Red-Black wavelet

51、 tran sform. Since illu min atio n have great in flue nce on feature extracti on and recog niti on based on PCA, so the recog niti oneffects can be enhan ced by illu min atio ncompe nsati on. Therefore, the comb in atio n of Red-Black wavelet tran sform, illumi natio n compe nsati on and PCA can ach

52、ieve more satisfactorysystem performance.Comparing with the traditionalmethod using wavelettransform and PCA , the recognition rate is enhanced obviously.5Con clusi onThe Red-Black wavelet transform divides the rectangular grid digital image into red and black blocks and uses the two-dimensional lifting form to con struct sub-ba nd. It is an effective way to wipe off the image relativity and gets the more sparser image. PCA extracts eige nv ector on the basis of the whole face grayscale relativity. The eige nv ector can retain the main classified information in the o