Paris《Face Detection Toolbox》(脸部识别工具箱)

应用背景在当今社会信息安全问题备受人们关注。自身安全和个人隐私保护成为这个时代的热门话题。基于密码、个人识别码和钥匙等传统的安全措施已不能完全满足社会要求。在这样一个背景下，人们把目光投向了生物特征识别技术 —— 利用人体固有的生理特征或行为特征来进行身份的鉴别或确认。关键技术 面部识别又称人脸识别、面像识别、面容识别等等，面部识别使用通用的摄像机作为识别信息获取装置。以非接触的方式获取识别对象的面部图像，计算机系统在获取图像后与数据库图像进行比对后完成识别过程。面部识别是基于生物特征的识别方式 ，与 请点击左侧文件开始预览 ！预览只提供20%的代码片段，完整代码需下载后查看 加载中 侵权举报

fdtool_release
chlbp2.c
demo_haar.m
detector_mlhmslbp_spyr.c
haar_gentleboost_binary_predict_cascade_memory.c
images
train
negatives
books1.jpg
example_train_histoint_feat_boost.m
gui
features_push_add_callback1.m
blasp.h
demo_mblbp.m
auroc.m
gauss.m
haar_gentleboost_binary_train_cascade_memory.c
eval_mblbp_subwindows.c
mblbp_gentleboost_binary_predict_cascade.c
homkermap.c
eval_mblbp.c
inv_integral_image.m
haar_dico_2.mat
int8tosparse.c
mblbp.c
haar_dico_4.mat
ddot.c
generate_data_cascade.m
eval_chlbp.c
plot_rectangle.m
chlbp_adaboost_binary_predict_cascade.c
haar_featlist.c
mblbp_ada_weaklearner.c
basicroc.m
ieJPGSearch.m
generate_fa_features.m
daxpy.c
haar_adaboost_binary_train_cascade.c
haar_gentleboost_binary_predict_cascade.c
haar_scale.c
generate_face_features.m
demo_detector_hmblbp.m
mblbp_gentle_weaklearner_old.c
omp.c
haar_adaboost_binary_predict_cascade_memory.c
fast_haar_ada_weaklearner.c
dscal.c
detector_mblbp.c
chlbp_gentleboost_binary_predict_cascade.c
demo_hmlblbgp.m
image_integral_standard.m
jensen_24x24.mat
demo_detector_hmblgp.m
eval_haar.c
area.c
linear_model_matlab.c
train_model.m
train_stage_cascade.m
mblbp_adaboost_binary_train_cascade.c
haar_gentle_weaklearner.c
rgb2gray.c
gaussianfilter.m
tron.h
mblbp_featlist.c
generate_nd_features.m
fast_rotate.c
mblbp_adaboost_binary_predict_cascade.c
fast_haar_adaboost_binary_train_cascade.c
imresize.c
qedit.h
demo_chlbp.m
viola_24x24.mat
readme.txt
nbfeat_haar.m
haar_ada_weaklearner_memory.c
html
demo_type_cascade_scaling_vs_interp_02.png
d2uint8_image.m
eval_model_dataset.m
detector_mlhmslgp_spyr.c
vcapg2.cpp
haar_ada_weaklearner.c
demo_integral_histogram.m
chlbp_gentleboost_binary_train_cascade.c
linear.cpp
chlbp.c
linear.h
demo_type_cascade_scaling_vs_interp.m
chlbp_adaboost_binary_train_cascade.c
eval_hmblbp_spyr_subwindow.c
demo_mblbp_variant_training.m
demo_haar_mblbp_training.m
demo_mlhmslbp_spyr_svm_training.m
demo_detector_haar.m
eval_hmblgp_spyr_subwindow.c
haar_adaboost_binary_predict_cascade.c
build_negatives.m
getmapping.m
haar_adaboost_binary_train_cascade_memory.c
test_eval_chlbp.m
train_cascade.m
dnrm2.c
generate_data_cascade_Xpos.m
haar_gentleboost_binary_train_cascade.c
perf_dr_fa.m
negatives.zip
image_standard.m
linear_model_matlab.h
mblbp_gentle_weaklearner.c
mblbp_gentleboost_binary_train_cascade.c
blas.h
model_09092013_R4.mat
mexme_fdt.m
haar_gentle_weaklearner_memory.c
haar_matG.m
detector_haar.c
homkertable.c
haar.c
train_cascade_Xpos.m
tron.cpp
display_database.m
ex_train_model_boats.m
eval_haar_subwindow.c
model_hmblbp_R4.mat
setup_fdt.m
demo_fine_threshold.m
train_dense.c
neldermead_error_fcn.m
license.txt