Master Thesis - Department of Computer Science

Table 5.4: Base classifier’s performance (in Percentage Accuracy) for four face and
fingerprint databases (two each).
Test Set PIE ORL F PA F PB
T est1 81.47 83.33 86.00 78.00
T est2 78.53 76.67 82.50 67.50
T est3 - - 80.00 60.00
T est4 - - 76.67 63.33
Table 5.5: Base classifier’s performance (in Percentage Accuracy) using LDA for the
face and fingerprint databases (two each).
Test Set PIE ORL F PA F PB
T est1 86.18 86.67 64.00 34.00
T est2 91.76 95.00 57.50 57.50
T est3 - - 63.33 55.33
T est4 - - 66.67 60.00
combinations as in Table 5.4. We witness the effectiveness of LDA and nonpara-
metric LDA on face classifier output space by comparing the performance of face
classifier using LDA, (see 1 st and 2 nd column of Table 5.5) and nonparametric LDA
(see 1 st and 2 nd column of Table 5.6) with the one not using any of the two (see
1 st and 2 nd column of Table 5.4). The same conclusion cannot be drawn in case
of fingerprint classifier, because of the deterioration of the performance of fingerprint
classifier on the application of LDA (see 3 rd and 4 th column of Table 5.5) and nonpara-
metric LDA (see 3 rd and 4 th column of Table 5.6) as compared to the performance of
base classifier without using the two (see 3 rd and 4 th column of Table 5.4). Henceforth
(as described in the algorithm in section 5.3.1), we use LDA and nonparametric LDA
only on face classifier space while keeping fingerprint classifier space unperturbed, and
then use the sum rule for combination. This can be considered to be a preprocessing
step before fusion on classifier space to improve their performance. One can argue
that we can use a better method for each classifier itself to make it strong. But that
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