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. 2021 May 27;24(4):1451–1473. doi: 10.1007/s10044-021-00991-z

Table 9.

Comparison of proposed works with LAG –LDA and other works on AR dataset

Method Illumination variation (S1) Expression change (S2) Scarves-Illumination(S4) Sunglasses-Illumination (S5)
LAG-LDA 90.67 (Exp 6) 93.25 (Exp 1) 73.33 (Exp 8) 92 (Exp 7)
Symbolic approach + PFFT 98.54 98.81 89.79 95.08
Symbolic approach + DGM 91.67 95.24 93.03 98.21
Symbolic approach + SGEF 96.46 97.14 94.67 83.92
Symbolic approach + GSGEF 96.46 95.48 95.90 87.50
NN + PFFT 100 100 98.98 85.25
NN + DG 91.88 96.91 94.26 42.86
NN + SGEF 97.71 98.33 97.95 91.07
NN + GSGEF 92.29 94.29 95.49 73.21
SVM + PFFT 99.58 99.52 90.82 94.67
SVM + DG 95.00 99.05 95.90 83.93
SVM + SGEF 95.42 96.90 92.62 80.36
SVM + GSGEF 89.90 92.86 91.39 69.64
PNN + PFFT 93.54 96.19 72.45 88.93
PNN + DG 96.04 95.00 93.44 55.35
PNN + SGEF 93.54 93.33 91.80 89.29
PNN + GSGEF 91.05 93.81 90.57 80.36

Bold values highlights the best recognition rate of proposed techniques compared to LAG-LDA