Table 6.

Recognition accuracy (%) of proposed face recognition approaches with GSGEF features on various combinations of AR

Individuals/labels	Total number samples used for testing	Symbolic modeling and similarity			Nearest neighbor			Support vector machine			Probabilistic neural network
		A	B	C	A	B	C	A	B	C	A	B	C
Illumination variation(Subset 1)	480	463	17	96.46	443	37	92.29	431	49	89.80	437	43	91.05
Expression (Subset 2)	420	401	19	95.48	396	24	94.29	390	30	92.86	394	26	93.81
Occlusion(Scarves + Sunglasses) (Subset 3)	(73 + 25) = 98	87	11	88.78	75	23	76.53	79	19	80.61	81	17	82.65
Scarves-Illumination(Subset 4)	244	234	10	95.90	233	11	95.49	223	21	91.39	221	23	90.57
Sunglasses-Illumination(Subset 5)	56	49	07	87.50	41	15	73.21	39	17	69.64	45	11	80.36
Total number of images (Subset 1 + Subset 2 + Subset 4 + Subset 5)	1200	1147	53	95.58	1113	87	92.75	1083	117	90.25	1097	103	91.42

A: Number of samples recognized correctly, B: Number of samples misclassified, C: Accuracy (%)