Master Thesis - Department of Computer Science

Table 3.9: Performance of Ekenel’s multiresolution face recognition [36] for Yale, PIE
and ORL databases based on the successful subbands determined on validation set.
Yale PIE ORL
Methods PRA EER PRA EER PRA EER
Original Face 81.67 7.78 58.85 17.05 80.83 3.33
Best Performing Subband 73.33 20.88 90.64 7.50 70.83 8.49
Data Fusion 66.67 23.60 31.15 29.59 69.17 7.07
Feature Fusion 65.00 24.90 27.12 30.96 71.67 7.68
Decision Fusion-Sum Rule 70.00 18.33 85.83 4.00 70.00 7.38
Decision Fusion-Product Rule 70.00 34.05 88.20 49.69 72.50 6.82
Decision Fusion-Max Rule 70.00 16.67 63.37 9.38 67.50 7.5
Proposed Method (with PCA) 95.00 3.33 85.38 4.51 88.33 4.64
Proposed Method (Overall) 100.00 0.47 95.32 1.15 91.67 1.67
3.5 Conclusion
In this chapter, we have proposed the use of subband face (reconstructed from selective
wavelet subbands) representation for the task of face recognition. This representation
is evaluated using PCA, 2D-PCA, LDA, 2D-LDA and DCV based subspace projec-
tions for Yale, ORL and PIE face databases. It is observed that the subband face
performs significantly better than the wavelet decomposed subbands of the face [36],
in the presence of variations in illumination, expression and pose. The novelty of
this method is that the subbands containing more of discriminatory information are
selected for face representation, whereas those with common features and redundant
information are discarded. Therefore subband face is an effective representation for
frontal face recognition achieving high recognition rate even under hard testing con-
ditions. The performance of the subband face can be further improved by obtaining
an optimal selection of discriminating subbands in case of full-tree decomposition of
the image using wavelets, use of modular [67] subband faces and also by exploring
more sophisticated criterion for subband selection.
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