Testing Recognition with cropped class images (eig 25 x 25)

Average Face :
Eigen Face 1 :
Eigen Face 2 :
Eigen Face 3 :
Eigen Face 4 :
Eigen Face 5 :
Eigen Face 6 :
Eigen Face 7 :
Eigen Face 8 :
Eigen Face 9 :
Eigen Face 10 :

Accuracies

Number of eigenfaces	Fraction
1	18.5185	5/27
2	44.4444	12/27
3	48.1481	13/27
4	48.1481	13/27
5	55.5556	15/27
6	48.1481	13/27
7	51.8519	14/27
8	66.6667	18/27
9	62.9630	17/27
10	74.0741	20/27
11	77.7778	21/27
12	74.0741	20/27
13	74.0741	20/27
14	74.0741	20/27
15	74.0741	20/27
16	74.0741	20/27
17	77.7778	21/27
18	74.0741	20/27
19	74.0741	20/27
20	74.0741	20/27
21	74.0741	20/27
22	74.0741	20/27
23	74.0741	20/27
24	74.0741	20/27
25	74.0741	20/27
26	74.0741	20/27
27	74.0741	20/27

Generally, the results improve with the number of eigenvectors used, although it is noted that after about 10 eigenvectors, the results stop improving. A balance must be struck between the benefit of adding another eigenvector, which adds to the runtime of the algorithm, and the accuracy of the system. In this case, a good tradeoff value (that does not seem to be relying on the eccentricity of this particular data set, and hence is not an overfit) is 10 eigenvectors.

Here are some images of some mistakes (10 eigvecs)