Table 5.

Recognition accuracy (%) of proposed face recognition approaches with SGEF features on subsets of AR dataset

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	469	11	97.71	458	22	95.42	449	31	93.54
Expression (Subset 2)	420	408	12	97.14	413	07	98.33	407	13	96.90	392	28	93.33
Occlusion(Scarves + Sunglasses) (Subset 3)	(73 + 25) = 98	89	09	90.82	83	15	84.69	79	19	80.61	76	22	77.55
Scarves-Illumination (Subset 4)	244	231	13	94.67	239	05	97.95	226	18	92.62	224	20	91.80
Sunglasses-Illumination (Subset 5)	56	47	09	83.92	51	05	91.07	45	11	80.36	50	06	89.29
Total number of images (Subset 1 + Subset 2 + Subset 4 + Subset 5)	1200	1149	51	95.75	1172	28	97.67	1136	64	94.67	1115	85	92.92

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