人脸识别 MATLAB代码
内部包含orl人脸数据库;朴素贝叶斯分类数值型数据、取点测比例距、训练数据集特征向量化、(PCA+adaboost PCA+SVM人脸识别(可用,全面))四种人脸识别相关的功能,经过测试均可用,4者代码相互之间没有关系,且第四个“测试成功@(PCA+adaboost PCA+SVM(可用,全面))”可以完整进行人脸识别,下载者根据功能需要选择使用
代码片段和文件信息
属性 大小 日期 时间 名称
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目录 0 2017-12-07 21:41 人脸识别 MATLAB代码
目录 0 2017-12-05 17:01 人脸识别 MATLAB代码orl_
目录 0 2017-12-05 17:01 人脸识别 MATLAB代码orl_s1
目录 0 2017-12-05 17:01 人脸识别 MATLAB代码orl_s10
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s101.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s1010.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s102.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s103.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s104.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s105.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s106.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s107.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s108.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s109.pgm
目录 0 2017-12-05 17:01 人脸识别 MATLAB代码orl_s11
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s111.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s1110.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s112.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s113.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s114.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s115.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s116.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s117.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s118.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s119.pgm
目录 0 2017-12-05 17:01 人脸识别 MATLAB代码orl_s12
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s121.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s1210.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s122.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s123.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s124.pgm
............此处省略1356个文件信息
function [Lhits] = ADABOOST_te(adaboost_modelte_func_handletest_settrue_labels)
%
% ADABOOST TESTING
%
% [Lhits] = ADABOOST_te(adaboost_modelte_func_handletrain_set
% true_labels)
%
% ‘te_func_handle‘ is a handle to the testing function of a
% learning (weak) algorithm whose prototype is shown below.
%
% [Lhitserror_rate] = test_func(modeltest_setsample_weightstrue_labels)
% model: the output of train_func
% test_set: a KxD dimensional matrix each of whose row is a
% testing sample in a D dimensional feature space.
% sample_weights: a Dx1 dimensional vector the i-th entry
% of which denotes the weight of the i-th sample.
% true_labels: a Dx1 dimensional vector the i-th entry of which
% is the label of the i-th sample.
% L: a Dx1-array with the predicted labels of the samples.
% hits: number of hits calculated with the comparison of L and
% true_labels.
% error_rate: number of misses divided by the number of samples.
%
% It is the corresponding testing
% module of the function that is specified in the training phase.
% ‘test_set‘ is a NxD matrix where N is the number of samples
% in the test set and D is the dimension of the feature space.
% ‘true_labels‘ is a Nx1 matrix specifying the class label of
% each corresponding sample‘s features (each row) in ‘test_set‘.
% ‘adaboost_model‘ is the model that is generated by the function
% ‘ADABOOST_tr‘.
%
% ‘L‘ is the likelihoods that are assigned by the ‘ADABOOST_te‘.
% ‘hits‘ is the number of correctly predicted labels.
%
% Specific Properties That Must Be Satisfied by The Function pointed
% by ‘func_handle‘
% ------------------------------------------------------------------
hypothesis_n = length(adaboost_model.weights);
sample_n = size(test_set1);
if nargin==4
class_n = length(unique(true_labels));
temp_L = zeros(sample_nclass_nhypothesis_n); % likelihoods for each weak classifier
% for each weak classifier likelihoods of test samples are collected
for i=1:hypothesis_n
[temp_L(::i)hitserror_rate] = te_func_handle(adaboost_model.parameters{i}...
test_setones(sample_n1)true_labels);
temp_L(::i) = temp_L(::i)*adaboost_model.weights(i);
end
L = sum(temp_L3);
hits = sum(likelihood2class(L)==true_labels);
else
class_n=2;
temp_L = zeros(sample_nclass_nhypothesis_n); % likelihoods for each weak classifier
% for each weak classifier likelihoods of test samples are collected
for i=1:hypothesis_n
temp_L(::i) = te_func_handle(adaboost_model.parameters{i}...
test_setones(sampl
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
目录 0 2017-12-07 21:41 人脸识别 MATLAB代码
目录 0 2017-12-05 17:01 人脸识别 MATLAB代码orl_
目录 0 2017-12-05 17:01 人脸识别 MATLAB代码orl_s1
目录 0 2017-12-05 17:01 人脸识别 MATLAB代码orl_s10
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s101.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s1010.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s102.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s103.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s104.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s105.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s106.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s107.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s108.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s109.pgm
目录 0 2017-12-05 17:01 人脸识别 MATLAB代码orl_s11
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s111.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s1110.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s112.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s113.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s114.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s115.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s116.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s117.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s118.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s119.pgm
目录 0 2017-12-05 17:01 人脸识别 MATLAB代码orl_s12
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s121.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s1210.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s122.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s123.pgm
文件 10318 2017-12-05 11:08 人脸识别 MATLAB代码orl_s124.pgm
............此处省略1356个文件信息
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