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| -rwxr-xr-x | report/paper.md | 24 |
1 files changed, 21 insertions, 3 deletions
diff --git a/report/paper.md b/report/paper.md index a31885c..f3bd549 100755 --- a/report/paper.md +++ b/report/paper.md @@ -173,7 +173,7 @@ can be observed in figure \label{cm}: \end{center} \end{figure} -Two examples of the outcome of Nearest Neighbor Classification are presented in figures \ref{nn-fail} and \ref{nn-succ}, +Two examples of the outcome of Nearest Neighbor Classification are presented in figures \ref{nn_fail} and \ref{nn_succ}, respectively one example of classification failure and an example of successful classification. @@ -181,7 +181,7 @@ classification. \begin{center} \includegraphics[width=7em]{fig/face2.pdf} \includegraphics[width=7em]{fig/face5.pdf} -\label{nn-fail} +\label{nn_fail} \caption{Failure case for NN. Test face left. NN right} \end{center} \end{figure} @@ -190,7 +190,7 @@ classification. \begin{center} \includegraphics[width=7em]{fig/success1.pdf} \includegraphics[width=7em]{fig/success1t.pdf} -\label{nn-fail} +\label{nn_succ} \caption{Success case for NN. Test face left. NN right} \end{center} \end{figure} @@ -314,6 +314,24 @@ LDA and it improves recognition performances with respect to PCA and LDA. # Question 3, LDA Ensemble for Face Recognition, PCA-LDA +In this section we will perform PCA-LDA recognition with NN classification. + +Varying the values of M_pca and M_lda we obtain the average recognition accuracies +reported in figure \ref{ldapca_acc}. Peak accuracy of 94.7% can be observed for M_pca=115, M_lda=41; +howeverer accuracies above 92% can be observed for M_pca values between 90 and 130 and +M_lda values between 30 and 50. + +Recognition accuracy is significantly higher than PCA, and the run time is roughly the same, +vaying between 0.11s(low M_pca) and 0.19s(high M_pca). + +\begin{figure} +\begin{center} +\includegraphics[width=20em]{fig/ldapca3dacc.pdf} +\caption{PCA-LDA NN Recognition Accuracy varying hyper-parameters} +\label{ldapca_acc} +\end{center} +\end{figure} + # Question 3, LDA Ensemble for Face Recognition, PCA-LDA Ensemble # References |