Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition

Table 1

Comparisons between the state-of-the-art models.

Ref.	Author and year	Biometrics traits	Database	Techniques	Level of fusion	Result (%)
[19]	Islam et al., 2013	3D ear and 3D face	FRGC 2.0Face and UND collection J	ICP	Feature level fusion	96.8
[24]	Islam et al., 2009	3D face and 3D ear	FRGC 2.0Face and UND collection J	ICP	Score level	98.7
[20]	Nazmeen, 2009	Face and ear	Individual	Karhunen–Loeve (KL) expansion, PCA	Decision level	96
[25]	Kyong et al., 2005	2D + 3D face	NA	ARMS	Match score	97.5
[26]	Ajmera, 2014	3D face	EURECOM, CurtinFace, and one internal database	SURF with adaptive histogram equalization	Match score	89.28, 98.07, and 81.00
[27]	Hui and Bhanu, 2007	3D ear	UCR and UND	Local surface patch, global to local registration, and ICP	Rank level	96.77 95.48
[28]	Rahman et al., 2016	Face	FRGC V2.0 and CK-AUC	2D krawtch UK moment(2DKCMs), PCA, LDA, and 2D-PCA	NA	98.70
[29]	Pujitha et al., 2010	Ear and face (2D + 3D)	Captured 2D images using Kinect	Eigenfaces	Feature fusion level	97
[9]	Ping and Bowyer, 2007	3D ear	UND	ICP	Feature level fusion	97.6
[30]	Wu et al., 2012	3D ear	UNDCollection J2	ICP fine alignment	Score level	97.59
[31]	Algabary et al., 2014	3D ear	UND	Iterative closest point and stochastic lustering matching (SCM)	Decision level	98.25
[32]	Drira et al., 2013	3D face	Biosecure residential workshop	ICP	Match score	97.25