Real-Time Gender Recognition for Juvenile and Adult Faces

Table 2

Different state-of-the-art techniques including featuring engineering and classification methods for facial gender recognition for FERET (adult faces) and UTK face dataset (juvenile faces).

Dataset	Authors	Feature extraction	Accuracy
		Classification
FERET	Leng and Wang [16]	Gabor-SVM, Fuzzy	98.0
	Rai and Khanna [17]	Gabor-2DPCA-SVM	98.2
	Aroussi et al. [18]	DWT-SVM	92
	Mohamed et al. [19]	DCT, DWT-SVM	95
	Wang et al. [20]	Gabor, SIFT -AdaBoost	97.0
	Ozbudak et al. [21]	DWT-PCA-FDA	9
	Lu and shi [22]	2D PCA-SVM (RBF)	94.8
	Bissoon and Viriri [23]	PCA-LDA	85
	Jain and Huang [24]	ICA-LDA	99.3
	Tapia and Perez [25]	LBP + Intensity+	99.1
		Shape-SVM
	Makinen and raisamo [26]	LBP, Haar-ANN, SVM	92.9
	Alamri et al. [27]	LBP, WLD-N.Neighbor	98.8
	Moeini et al. [28]	LGBP-SVM	98.5
	Patel et al. [29]	CoLBP-SVM	93.9
	Annalakshmi et al. [30]	SLBP + HOG-SVM	97.6
	Aslam et al. [31]	CNN (VGG-16)	98.9
	Afifi and Abdelhamed [32]	Foggy face-Deep CNN	99.3
UTK	Swaminathan et al. [33]	Face Embed (FE)-SVM	88.4
		FE-logistic regression	92.4
		FE-naive-bayes	89.4
		Fe-KNN	97.0
		FE-decision trees	93.2
	Teru and Chakraborty [1]	CNN	89.5
		CNN-WL (weight loss)	88.8
		Fader CNN	84.8
	Song and Shmatikov [34]	CNN	90.4
	Bragman et al. [35]	CNN	92.5
	Nagpal et al. [36]	CNN	94.6
	Das et al. [37]	MTCNN	98.2
	Abdolrashidi [38]	Ensemble of ResNet	96.5
AD	Hassner et al. [39]	LBP + FPLBP-SVM	79.3
	Khan et al. [40]	PCS-RDF	91.4
FG-NET	Nayak and Indiramma [41]	PCA	61.13