基于Matlab 直方图Histogram的人脸识别程序

Algorithm Description Recognizing objects from large image databases, histogram based methods have proved simplicity and usefulness in last decade. Initially, this idea was based on color histograms that were launched by swain [1]. This algorithm presents the first part of our proposed technique named as “Histogram processed Face Recognition” [2] For training, grayscale images with 256 gray levels are used. Firstly, frequency of every gray-level is computed and stored in vectors for further processing. Secondly, mean of consecutive nine frequencies from the stored vectors is calculated and are stored in another vectors for later use in testing phase. This mean vector is used for calculating the absolute differences among the mean of trained images and the test image. Finally the minimum difference found identifies the matched class with test image. Recognition accuracy is of 99.75% (only one mis-match i.e. recognition fails on image number 4 of subject 17) [1] M. J. Swain and D. H. Ballard, “Indexing via color histogram”, In Proceedings of third international conference on Computer Vision (ICCV), pages 390–393, Osaka, Japan, 1990. [2] Fazl-e-Basit, Younus Javed and Usman Qayyum, "Face Recognition using processed histogram and phase only correlation ", 3rd IEEE International Conference on Emerging Technology pp. 238-242

function varargout = FR_Processed_histogram（varargin）
gui_Singleton = 1;
gui_State = struct（‘gui_Name‘       mfilename ...
    ‘gui_Singleton‘  gui_Singleton ...
    ‘gui_OpeningFcn‘ @FR_Processed_histogram_OpeningFcn ...
    ‘gui_OutputFcn‘  @FR_Processed_histogram_OutputFcn ...
    ‘gui_LayoutFcn‘  []  ...
    ‘gui_Callback‘   []）;
if nargin && ischar（varargin{1}）
    gui_State.gui_Callback = str2func（varargin{1}）;
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn（gui_State varargin{:}）;
else
    gui_mainfcn（gui_State varargin{:}）;
end

function FR_Processed_histogram_OpeningFcn（hobject eventdata handles varargin）
handles.output = hobject;
guidata（hobject handles）;
global total_sub train_img sub_img max_hist_level bin_num form_bin_num;
total_sub = 40;
train_img = 200;
sub_img = 10;
max_hist_level = 256;
bin_num = 9;
form_bin_num = 29;

function varargout = FR_Processed_histogram_OutputFcn（hobject eventdata handles）
varargout{1} = handles.output;
%% 响应Training按钮
function train_button_Callback（hobject eventdata handles）
global train_processed_bin;
global total_sub train_img sub_img max_hist_level bin_num form_bin_num;
train_processed_bin（form_bin_numtrain_img） = 0;
K = 1;
train_hist_img = zeros（max_hist_level train_img）;
for Z=1:1:total_sub  % 共有40组图像
    for X=1:2:sub_img    %只是训练每组图像下的奇数图像
        I = imread（ strcat（‘ORL\S‘int2str（Z）‘\‘int2str（X）‘.bmp‘） ）;
        [rows cols] = size（I）;
        for i=1:1:rows
            for j=1:1:cols
                if（ I（ij） == 0 ）
                    train_hist_img（max_hist_level K） =  train_hist_img（max_hist_level K） + 1;
                else
                    train_hist_img（I（ij） K） = train_hist_img（I（ij） K） + 1;
                end
            end
        end
        K = K + 1;
    end
end
[r c] = size（train_hist_img）;
sum = 0;
for i=1:1:c
    K = 1;
    for j=1:1:r
        if（ （mod（jbin_num）） == 0 ）
            sum = sum + train_hist_img（ji）;
            train_processed_bin（Ki） = sum/bin_num;
            K = K + 1;
            sum = 0;
        else
            sum = sum + train_hist_img（ji）;
        end
    end
    train_processed_bin（Ki） = sum/bin_num;
end
display （‘Training Done‘）
save ‘train‘  train_processed_bin;
%% 响应Testing按钮
function Testing_button_Callback（hobject eventdata handles）
global train_img max_hist_level bin_num form_bin_num;
global train_processed_bin;
global filename pathname I
load ‘train‘
test_hist_img（max_hist_level） = 0;
test_processed_bin（form_bin_num） = 0;
[rows cols] = size（I）;
for i=1:1:rows
    for j=1:1:cols
        if（ I（ij） == 0 ）
            test_hist_img（max_hist_level） =  test_hist_img（max_hist_level） + 1;
        else
            test_hist_img（I（ij）） = test_hist_img（I（ij）） + 1;
        end
    end
end
[r c] = size（test_hist_img）;
sum = 0;
K = 1;
%此处和Train中的不同为此处为只有一个图像
for j=1:1:c
    if（ （mod（jbin_num）） == 0 ）
        s

 属性            大小     日期    时间   名称
----------- ---------  ---------- -----  ----
     目录           0  2011-07-03 10:53  基于Matlab 直方图Histogram的人脸识别程序\
     文件       25600  2011-03-31 16:57  基于Matlab 直方图Histogram的人脸识别程序\Face Recognition Algorithm Description.doc
     文件      662310  2008-12-11 16:13  基于Matlab 直方图Histogram的人脸识别程序\Face_recognition_Histogram_processed_GUI.bmp
     文件        4981  2011-03-31 19:28  基于Matlab 直方图Histogram的人脸识别程序\FR_Processed_histogram.asv
     文件        8416  2011-03-31 19:25  基于Matlab 直方图Histogram的人脸识别程序\FR_Processed_histogram.fig
     文件        5040  2011-03-31 19:31  基于Matlab 直方图Histogram的人脸识别程序\FR_Processed_histogram.m
     目录           0  2011-06-17 18:45  基于Matlab 直方图Histogram的人脸识别程序\ORL\
     目录           0  2011-06-17 18:44  基于Matlab 直方图Histogram的人脸识别程序\ORL\S1\
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     目录           0  2011-06-17 18:44  基于Matlab 直方图Histogram的人脸识别程序\ORL\S3\
     文件        2784  2011-06-17 18:35  基于Matlab 直方图Histogram的人脸识别程序\ORL\S3\1.jpg
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     目录           0  2011-06-17 18:45  基于Matlab 直方图Histogram的人脸识别程序\ORL\S4\
     文件        2457  2011-06-17 18:35  基于Matlab 直方图Histogram的人脸识别程序\ORL\S4\1.jpg
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