用Matlab实现的人脸定位，急，期待高手解答

大概看了一下，这个程序编得一团糟，肯定有问题~~~~看完头两个IF我已经疯了。编算法人的思路我说一下你就明白了。

读进一幅图，二进制化，也就是说比如200X120的矩阵，只有黑白，要么1，要么0.

用网格把它打成10X10的格子图，每格就有个20X12的小矩阵。然后

for i = 1:10

...

for j = 1:10

这两个FOR就是对这100个格子逐个进行分析，从格子1，一直到格子100，但实际上后面就发现是对对这100个格子的边缘格子进行分析 ，中间不动（就是假设 边缘最没用，脸液轮不会出现在那儿，能扔就扔掉）

if (y2<=c | y2>=9*c) | (x1==1 | x2==r*10) % 保证是在边缘的格子里面

loc=find(BW(x1:x2, y1:y2)==0)% 这个格子（矩阵）里，有多少值为0的元素，即为背景元素，没用的，不是人脸。（所以这段 程序开头写了“将背景部分弱化”。

[o p]=size(loc)% 噢，找到了这么多个0

pr=o*100/s

% 用pr值 来判断是否应该将这整个格子全部值 赋为0，比如一个格子里面只有几个1（比如几根头发），其它全是无用的信息0，那么干脆就把这个格子全部变成0，免得影响判断。pr的定义是有问闹竖信题的，因为o永远小于s（你可以自己算算），所以所有的边缘格都会强行被赋为0，就是“全黑了”。

if pr<=100

BW(x1:x2, y1:y2)=0

r1=x1r2=x2s1=y1s2=y2% 这句话P用没有，整个程序前后都没出现r1,r2,s1,s2，在这儿定义有什么用？

pr1=0%同样是句P话，其他地方都没出现过pr1

end

end

y1=y1+c

y2=y2+c

end

x1=x1+r

x2=x2+r

然后这几句就是格子赋值 结束，继续前进，找下一个格子呗，又回到初始。。。

所以，上面这段程序，什么人脸不人脸检测的，就是把边缘全部变黑而己……

所以下纤姿面我也看不进去了。。。

补充：

还是说完吧，后面一段程序，其实就是把所有变黑的边缘的边界给勾了出来。没有任何新东西。。。

所以你这段程序，就是先把边缘全部抹黑，然后勾出这个边缘的边界，画出来，就行了。没有任何“人脸定位”的东西，就是假设人脸在图的中间，边缘没有任何信息。。。仅此而己~~~

function pca (path, trainList, subDim)

%

% PROTOTYPE

% function pca (path, trainList, subDim)

%

% USAGE EXAMPLE(S)

% pca ('C:/FERET_Normalised/', trainList500Imgs, 200)

%

% GENERAL DESCRIPTION

% Implements the standard Turk-Pentland Eigenfaces method. As a final

% result, this function saves pcaProj matrix to the disk with all images

% projected onto the subDim-dimensional subspace found by PCA.

%

% REFERENCES

% M. Turk, A. Pentland, Eigenfaces for Recognition, Journal of Cognitive

% Neurosicence, Vol. 3, No. 1, 1991, pp. 71-86

%

% M.A. Turk, A.P. Pentland, Face Recognition Using Eigenfaces, Proceedings

% of the IEEE Conference on Computer Vision and Pattern Recognition,

% 3-6 June 1991, Maui, Hawaii, USA, pp. 586-591

%

%

% INPUTS:

% path - full path to the normalised images from FERET database

% trainList - list of images to be used for training. names should be

% without extension and .pgm will be added automatically

% subDim - Numer of dimensions to be retained (the desired subspace

% dimensionality). if this argument is ommited, maximum

% non-zero dimensions will be retained, i.e. (number of training images) - 1

%

% OUTPUTS:

% Function will generate and save to the disk the following outputs:

% DATA - matrix where each column is one image reshaped into a vector

% - this matrix size is (number of pixels) x (number of images), uint8

% imSpace - same as DATA but only images in the training set

% psi - mean face (of training images)

% zeroMeanSpace - mean face subtracted from each row in imSpace

% pcaEigVals - eigenvalues

% w - lower dimensional PCA subspace

% pcaProj - all images projected onto a subDim-dimensional space

%

% NOTES / COMMENTS

% * The following files must either be in the same path as this function

% or somewhere in Matlab's path:

% 1. listAll.mat - containing the list of all 3816 FERET images

%

% ** Each dimension of the resulting subspace is normalised to unit length

%

% *** Developed using Matlab 7

%

%

% REVISION HISTORY

% -

%

% RELATED FUNCTIONS (SEE ALSO)

% createDistMat, feret

%

% ABOUT

% Created: 03 Sep 2005

% Last Update: -

% Revision: 1.0

%

% AUTHOR: Kresimir Delac

% mailto: kdelac@ieee.org

% URL: http://www.vcl.fer.hr/kdelac

%

% WHEN PUBLISHING A PAPER AS A RESULT OF RESEARCH CONDUCTED BY USING THIS CODE

% OR ANY PART OF IT, MAKE A REFERENCE TO THE FOLLOWING PAPER:

% Delac K., Grgic M., Grgic S., Independent Comparative Study of PCA, ICA, and LDA

% on the FERET Data Set, International Journal of Imaging Systems and Technology,

% Vol. 15, Issue 5, 2006, pp. 252-260

%

% If subDim is not given, n - 1 dimensions are

% retained, where n is the number of training images

if nargin <3

subDim = dim - 1

end

disp(' ')

load listAll

% Constants

numIm = 3816

% Memory allocation for DATA matrix

fprintf('Creating DATA matrix\n')

tmp = imread ( [path char(listAll(1)) '.pgm'] )

[m, n] = size (tmp)% image size - used later also!!!

DATA = uint8 (zeros(m*n, numIm))% Memory allocated

clear str tmp

% Creating DATA matrix

for i = 1 : numIm

im = imread ( [path char(listAll(i)) '.pgm'] )

DATA(:, i) = reshape (im, m*n, 1)

end

save DATA DATA

clear im

% Creating training images space

fprintf('Creating training images space\n')

dim = length (trainList)

imSpace = zeros (m*n, dim)

for i = 1 : dim

index = strmatch (trainList(i), listAll)

imSpace(:, i) = DATA(:, index)

end

save imSpace imSpace

clear DATA

% Calculating mean face from training images

fprintf('Zero mean\n')

psi = mean(double(imSpace'))'

save psi psi

% Zero mean

zeroMeanSpace = zeros(size(imSpace))

for i = 1 : dim

zeroMeanSpace(:, i) = double(imSpace(:, i)) - psi

end

save zeroMeanSpace zeroMeanSpace

clear imSpace

% PCA

fprintf('PCA\n')

L = zeroMeanSpace' * zeroMeanSpace% Turk-Pentland trick (part 1)

[eigVecs, eigVals] = eig(L)

diagonal = diag(eigVals)

[diagonal, index] = sort(diagonal)

index = flipud(index)

pcaEigVals = zeros(size(eigVals))

for i = 1 : size(eigVals, 1)

pcaEigVals(i, i) = eigVals(index(i), index(i))

pcaEigVecs(:, i) = eigVecs(:, index(i))

end

pcaEigVals = diag(pcaEigVals)

pcaEigVals = pcaEigVals / (dim-1)

pcaEigVals = pcaEigVals(1 : subDim)% Retaining only the largest subDim ones

pcaEigVecs = zeroMeanSpace * pcaEigVecs% Turk-Pentland trick (part 2)

save pcaEigVals pcaEigVals

% Normalisation to unit length

fprintf('Normalising\n')

for i = 1 : dim

pcaEigVecs(:, i) = pcaEigVecs(:, i) / norm(pcaEigVecs(:, i))

end

% Dimensionality reduction.

fprintf('Creating lower dimensional subspace\n')

w = pcaEigVecs(:, 1:subDim)

save w w

clear w

% Subtract mean face from all images

load DATA

load psi

zeroMeanDATA = zeros(size(DATA))

for i = 1 : size(DATA, 2)

zeroMeanDATA(:, i) = double(DATA(:, i)) - psi

end

clear psi

clear DATA

% Project all images onto a new lower dimensional subspace (w)

fprintf('Projecting all images onto a new lower dimensional subspace\n')

load w

pcaProj = w' * zeroMeanDATA

clear w

clear zeroMeanDATA

save pcaProj pcaProj

---