Palmprint Recognition Based on Subspace and Texture Feature Fusion

Input: data matrix X, parameter λ₁
Initialization:A=0; E=0; Y=0; α=0.1; ρ=1.01;P=arg $\begin{matrix} \min_{P} \end{matrix}$ tr $\begin{matrix} [P^{T} (S_{w} - λ S_{b}) P] \end{matrix}$ s.t. P^TP=I;α_max=10⁵;λ=10^-4
while not converged do1. Update A by using Eq. (17);2. Update P by using Eq. (19);3. Update E by using Eq. (21);4. Update Y, α by using Eqs. (22) and (23), respectively
end while
Output: P,A,E

Table 2. EER for single feature and fusion feature recognitions%
View table
Table 2. EER for single feature and fusion feature recognitions%
Database Subspacefeature Texturefeature Fusionfeature
PolyU 1.5082 0.3853 0.3440
Self-builtnon-contact 2.5176 1.5168 1.4922

Table 3. Comparison of EER and recognition time between proposed and other methods

View table

Table 3. Comparison of EER and recognition time between proposed and other methods

Method	PolyUdatabase		Self-built non-contactdatabase
Method	EER /%	Recognitiontime /s	EER /%	Recognitiontime /s
PCA	3.5064	0.3165	5.6432	0.3091
2DGabor	2.5242	0.8189	3.1340	0.9136
LDA	3.2340	0.2674	4.3759	0.3010
LBP	3.1754	0.2424	4.2591	0.2911
RLDA	1.5082	0.2229	2.5176	0.2294
LOBP	0.3853	0.1628	1.5168	0.1801
LBP+2DLPP^[9]	0.5077	0.4844	1.6326	0.5794
GGF^[10]	0.4761	0.3505	1.6073	0.3968
WACS-LBP+WSRC^[11]	0.4085	0.3239	1.5384	0.3479
ProposedRLDA+LOBP	0.3440	0.3069	1.4922	0.3127

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Xinchun Li, Hongyan Ma, Sen Lin. Palmprint Recognition Based on Subspace and Texture Feature Fusion[J]. Laser & Optoelectronics Progress, 2019, 56(7): 071007

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