Master Thesis - Department of Computer Science

(noglasses)(centerlight) (happy) (normal)
(1) (2) (3) (4)
(1) (2) (3)
Figure 3.9: Training samples for three databases: first, second and third row shows
the training set for Yale, PIE, and ORL databases, respectively.
The performance of subject-specific subband face on ORL database, which has
a considerable amount of pose variation is given in Table 3.5. The maximum PRA
obtained on this database is 91.67% for 2D-LDA using the first criterion. The same
method also gives the minimum EER of 1.67%. LDA is observed to provide a maxi-
mum PRA gain of 10.00% with fourth criterion on this database.
3.4.3 Comparison with Ekenel’s Multiresolution Face Recognition [36]
We have also compared the performance of our approach with a recent wavelet-based
method denoted as Ekenel’s multiresolution face recognition [36]. They used 2D
DWT to decompose the original image upto level-III and obtained A1, H1, V1, D1 in
the first level, A2, H2, V2, D2 in second level, and at the third level they generated
16 subband images by decomposing A2, H2, V2 and D2 further and thus obtained a
total of 24 subsampled images. Daubechies 4-tap wavelet was used for wavelet tree
decomposition. For each subband they extracted features using PCA and ICA, and
nearest neighborhood criterion was used for classification. They used three different
64