Complementary feature sets for optimal face recognition

EURASIP Journal on Image and Video Processing

Table 12 Performance comparison (%) of the proposed approaches with recent methods on Yale and ORL databases

Method	Yale database	ORL database
Two-dimensional LDA (2D-LDA) [42]	86.57	92.50
2D-WLDA [42]	88.00	93.50
2D-DWLDA [42]	89.33	94.00
Direct LDA (DLDA) [21]	93.20	92.50
Enhanced Fisher linear discriminant model (EFM) [21]	93.90	92.50
Intrinsicfaces [43]	74.00	97.00
Combined feature Fisher classifier (CF²C) [21]	96.90	96.80
Feature Fisher classifier (F³C) [20]	96.4	94.9
Block based S-P^a[25]	100.0^a	99.0
Algorithm A (WMs) [41]	–	93.5
Algorithm B (CWMs) [41]	–	96.0
ZM_magLBP	97.56	99.20
ZM_magPhaseLBP	96.78	98.25
ZM_componentLBP	95.67	98.95
ZM_magLTP	97.56	98.85
ZM_magPhaseLTP	97.00	98.35
ZM_componentLTP	97.00	99.20

Comparison of recognition performance for five random training images. ^aThe results are presented on only one random set of five images in the training set and all the remaining in the test set.