Add peak accuracy value

1 files changed, 15 insertions, 3 deletions

diff --git a/report/paper.md b/report/paper.md
index 113dfa6..e201252 100755
--- a/report/paper.md
+++ b/report/paper.md
@@ -38,8 +38,8 @@ for our standard seed can be observed below.
 To perform face recognition we choose the best M eigenvectors
 associated with the largest eigenvalues. We tried
 different values of M, and we found an optimal point for
-M=120. After such value the accuracy starts to flaten, with
-some exception for points at which accuracy decreases. 
+M=42 with accuracy=66.3%. After such value the accuracy starts 
+to flaten, with some exceptions for points at which accuracy decreases. 
 WE NEED TO ADD PHYSICAL MEANINGS
 
 ![Recognition Accuracy of Test data varying M](fig/accuracy.pdf "Accuracy1")
@@ -80,7 +80,19 @@ PCA &Fast PCA\\
 It can be proven that the eigenvalues and eigenvectors
 obtain are the same: ##PROVE
 
-Reconstruction is then performed on a chosen 
+Using the computational method for fast PCA, face reconstruction is then performed.
+The quality of reconstruction will depend on the amount of eigenvectors picked.
+The results of varying M can be observed in the picture below. A face from class 
+number 21 is reconstructed as shown below withrespective M values of M=10, M=100,
+M=200, M=300. The last picture is the original face.
+
+![Reconstructed Face](fig/face160rec.pdf)
+
+It is already observable that the improvement in reconstruction is marginal for M=200 
+and M=300. For such reason choosing M close to 100 is good enough for such purpose.
+
+IT HAS TO BE DONE FOR MORE FACE IMAGES
+
 # Cites