Research Article

Face Recognition Using Double Sparse Local Fisher Discriminant Analysis

Table 1

The top recognition rates (%) and the corresponding dimensions on Yale database by different methods (mean ± std).

2 trains3 trains4 trains5 trains6 trains

PCA46.30 ± 3.26
()
51.58 ± 4.00
()
56.19 ± 4.16
()
57.33 ± 4.60
()
62.40 ± 4.06
()
LDA45.11 ± 3.46
()
62.08 ± 4.31
()
70.86 ± 4.88
()
71.44 ± 5.19
()
77.22 ± 3.47
()
LPP45.26 ± 3.52
()
62.83 ± 4.22
()
70.57 ± 4.59
()
72.22 ± 3.81
()
78.00 ± 3.46
()
LFDA45.11 ± 3.46
()
62.50 ± 5.43
()
71.33 ± 5.07
()
72.33 ± 5.09
()
78.27 ± 3.72
()
SPCA43.19 ± 3.22
()
49.83 ± 4.08
()
54.95 ± 3.70
()
56.78 ± 3.33
()
61.87 ± 5.11
()
SLDA51.19 ± 5.78
()
63.85 ± 3.47
()
72.00 ± 4.76
()
72.56 ± 2.29
()
78.40 ± 2.42
()
SPP46.59 ± 5.36
()
52.92 ± 3.63
()
57.67 ± 3.54
()
58.48 ± 3.76
()
64.53 ± 4.63
()
DSNPE50.74 ± 5.26
()
63.58 ± 3.64
()
73.62 ± 5.04
()
75.89 ± 2.82
()
80.08 ± 2.61
()
DSLFDA53.11 ± 5.36
()
65.17 ± 3.68
()
73.24 ± 5.03
()
74.44 ± 3.51
()
81.47 ± 2.77
()