Table 4.

Comparison of different multi attribute (age and gender) based facial recognition models. The table mentions the state-of-the-art methods including name of author, year, dataset, method used and performance achieved in term of accuracy and MAE

Authors	Methods	Dataset	Accuracy/MAE
Eidinger et al. [26]	LBP	Adience	Age group: 45.1%
	four-patch	(13,000; 3,300)	Gender: 77.8%
	LBP SVM	Images of Groups	Age group:66.6%
	Dropout	(3,500; 1,050)	Gender:88.6%
Guo and Mu [38]	BIF	MORPH II	Gender: 98.5%
			Age:3.92
			Age group:70.0%
Han et al. [44]	BIF	MORPH II	Age:77.4%
	SVM		Gender:97.6%
Yi et al. [100]	Multi-Scale	MORPH II	Age:3.63
	CNN	(10,530, 44,602)	Gender:98.0%
Levi et al. [60]	CNN(3Conv	Adience	Gender:86.8%
	,2FC)One		Age group:50.7%
	CNN/attribute
Uricar et al. [92]	VGG-16	ChaLearn LAP	Gender: 89.2%
	One SVM/		Age:0.24 error
	attribute
Wang et al. [94]	AlexNet +	MORPH II	Gender: 98.0%
	DMTL		Age : 85.3%
Li et al. [61]	Tree CNN	MORPH II	Gender:98.4%
	features		Age:3.61 MAE
Shin et al. [86]	CNN,SVM	Faces of Asian	Gender:75.91%
		Non Asian	Age:54.98%
		Celebrity
Zhang et al. [103]	CNN,RoR	Adience	Gender: 93.24%
			Age:66.74%
Liao et al. [65]	LDNN	LFW	Gender:77.79%
			Age:39.50%
Han et al. [43]	DMTL	LFW+	Gender:96.7%
	based on		Age:75.0% MAE:4.5
	modified	MORPH II	Gender:98.3%
	AlexNet		Age:85.3%,MAE:3.0
Duan et al. [25]	CNN+ELM	MORPH	Gender: 87.3%
			Age MAE:3.44
Das et al. [22]	MTCNN	UTKFace	Gender: 98.23%
			Age:70.1%
Lee et al. [58]	LMTCNN	Adience	Gender: 85.16%
			Age:70.78%
Debgupta et al. [24]	ResNet	APPA-REAL	Gender: 96.26%
			Age MAE:1.65
Debgupta et al. [24]	ResNet	APPA-REAL	Gender: 96.26%
			Age MAE:1.65
Gurnani et al. [41]	Sailency Map	Adience	Gender:62.11%
	+AlexNet		Age:91.8%
Yoo et al. [102]	CMT + LE	MORPH-II	Gender: 99.28
			Age MAE:2.89
Agbo et al. [4]	Deep CNN	OIU-Adience	Gender:96.2%
			Age:93.8%
Khan et al. [53]	MCFP-	Adience	Gender:93.6%
	DCNNs		Age:69.4%
		LFW	Gender:94.1%
		FERET	Gender:100%