文件名称:hmm_matlab
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基于隐马尔科夫模型HMM的人脸识别程序，使用Matlab编写，提取的人脸识别特征为DCT系数-it is face detection codes based on HMM Model ，you can run iton Matlab，the feature from face is DCT
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下载文件列表
hmm_matlab
..........\dct_hmm.m
..........\dct_test1.m
..........\em_converged.m
..........\fwdback.m
..........\isposdef.m
..........\KPMstats
..........\........\#histCmpChi2.m#
..........\........\beta_sample.m
..........\........\chisquared_histo.m
..........\........\chisquared_prob.m
..........\........\chisquared_readme.txt
..........\........\chisquared_table.m
..........\........\clg_Mstep.m
..........\........\clg_Mstep_simple.m
..........\........\clg_prob.m
..........\........\condGaussToJoint.m
..........\........\condgaussTrainObserved.m
..........\........\condgauss_sample.m
..........\........\cond_indep_fisher_z.m
..........\........\convertBinaryLabels.m
..........\........\cwr_demo.m
..........\........\cwr_em.m
..........\........\cwr_predict.m
..........\........\cwr_prob.m
..........\........\cwr_readme.txt
..........\........\cwr_test.m
..........\........\dirichletpdf.m
..........\........\dirichletrnd.m
..........\........\dirichlet_sample.m
..........\........\distchck.m
..........\........\eigdec.m
..........\........\est_transmat.m
..........\........\fit_paritioned_model_testfn.m
..........\........\fit_partitioned_model.m
..........\........\fwdback.m
..........\........\gamma_sample.m
..........\........\gaussian_prob.asv
..........\........\gaussian_prob.m
..........\........\gaussian_sample.m
..........\........\histCmpChi2.m
..........\........\histCmpChi2.m~
..........\........\KLgauss.m
..........\........\linear_regression.m
..........\........\logist2.m
..........\........\logist2Apply.m
..........\........\logist2ApplyRegularized.m
..........\........\logist2Fit.m
..........\........\logist2FitRegularized.m
..........\........\logistK.m
..........\........\logistK_eval.m
..........\........\marginalize_gaussian.m
..........\........\matrix_normal_pdf.m
..........\........\matrix_T_pdf.m
..........\........\mc_stat_distrib.m
..........\........\mhmm_em.m
..........\........\mhmm_logprob.m
..........\........\mixgauss_classifier_apply.m
..........\........\mixgauss_classifier_train.m
..........\........\mixgauss_em.m
..........\........\mixgauss_init.m
..........\........\mixgauss_Mstep.m
..........\........\mixgauss_prob.asv
..........\........\mixgauss_prob.m
..........\........\mixgauss_prob_test.m
..........\........\mixgauss_sample.m
..........\........\mkPolyFvec.m
..........\........\mk_unit_norm.m
..........\........\multinomial_prob.m
..........\........\multinomial_sample.m
..........\........\multipdf.m
..........\........\multirnd.m
..........\........\normal_coef.m
..........\........\partial_corr_coef.m
..........\........\parzen.m
..........\........\parzenC.c
..........\........\parzenC.dll
..........\........\parzenC.mexglx
..........\........\parzenC_test.m
..........\........\parzen_fit_select_unif.m
..........\........\pca.m
..........\........\README.txt
..........\........\rndcheck.m
..........\........\sample.m
..........\........\sample_discrete.m
..........\........\sample_gaussian.m
..........\........\standardize.m
..........\........\standardize.m~
..........\........\student_t_logprob.m
..........\........\student_t_prob.m
..........\........\test_dir.m
..........\........\unidrndKPM.m
..........\........\unidrndKPM.m~
..........\........\unif_discrete_sample.m
..........\........\viterbi_path.m
..........\........\weightedRegression.m
..........\KPMtools
..........\........\approxeq.m
..........\........\approx_unique.m
..........\........\argmax.m