Finger-Knuckle-Print Recognition Based on NSST and Tetrolet Energy Features

Yuan Wang; Sen Lin

doi:10.3788/LOP202158.0210019

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 2 >Page 0210019 > Article

Laser & Optoelectronics Progress
Vol. 58, Issue 2, 0210019 (2021)

Finger-Knuckle-Print Recognition Based on NSST and Tetrolet Energy Features

Yuan Wang^* and Sen Lin

Author Affiliations

School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China

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DOI: 10.3788/LOP202158.0210019 Cite this Article Set citation alerts

Yuan Wang, Sen Lin. Finger-Knuckle-Print Recognition Based on NSST and Tetrolet Energy Features[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210019 Copy Citation Text

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Fig. 1. Five basic Tetrominoes

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Fig. 2. Flow chart of our method

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Fig. 3. Result processed by our method. (a) Image processed by our method; (b) energy characteristic surface

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Fig. 4. Energy difference surfaces under different conditions. (a) Image1 of the same person; (b) image2 of the same person (c) image of the different person; (d) matching energy difference surface; (e) mismatching energy difference surface

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Fig. 5. Example of HKPU-FKP database ROI. (a) Original database; (b) noise database

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Fig. 6. Histogram equalization contrast. (a) Image before equalization; (b) histogram before equalization; (c) image after equalization; (d) histogram after equalization

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Fig. 7. Matching curve and ROC (HKPU-FKP and noise database). (a) Matching curve;(b) ROC

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Fig. 8. Example of IIT Delhi-FK database. (a) Original database; (b) noise database

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Fig. 9. Matching curve and ROC (IIT Delhi-FK and noise database). (a) Matching curve; (b) ROC

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Fig. 10. Example of HKPU-CFK ROI database. (a) Original database; (b) noise database

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Fig. 11. Matching curve and ROC (HKPU-CFK and noise database). (a) Matching curve; (b) ROC

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Fig. 12. Correct recognition rate and matching time. (a) Correct recognition rate; (b) matching time

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Algorithm	Gabor+LDA	PCA	Haar	LBP	Surfacelet	Tetrolet	2DPCA	NTES
W_CRR/%	96.8721	94.4564	96.6701	94.2659	95.2381	96.1901	98.1025	98.0392
Matching time/s	0.1452	0.0987	0.0444	0.0126	0.1562	0.0926	0.1345	0.0497

Table 1. Recognition rate and matching time (HKPU-FKP original database)

Algorithm	Gabor+LDA	PCA	Haar	LBP	Surfacelet	Tetrolet	2DPCA	NTES
W_CRR/%	94.2705	93.2167	95.7612	92.8075	93.4955	94.2658	96.2851	97.7328
Matching time/s	0.1529	0.1024	0.0768	0.0275	0.1762	0.1121	0.1672	0.0526

Table 2. Recognition rate and matching time (HKPU-FKP noise database)

Algorithm	LGIC^[19]	LBP	PCA	LGIC2^[20]	2DPCA	NTES
W_EER/%	0.402	3.5046	4.2591	0.358	3.4705	2.5646
Matching time/s	0.2618	0.0126	0.0987	<0.5000	0.1345	0.0497

Table 3. Equal error rate and matching time (HKPU-FKP database)

Algorithm	Gabor+LDA	PCA	Haar	LBP	Surfacelet	Tetrolet	2DPCA	NTES
W_CRR/%	96.9523	94.0323	95.2658	94.5696	95.6586	96.9633	97.0256	98.0158
Matching time/s	0.1523	0.0845	0.060	0.0245	0.1145	0.1295	0.1562	0.0552

Table 4. Recognition rate and matching time (IIT Delhi-FK original database)

Algorithm	Gabor+LDA	PCA	Haar	LBP	Surfacelet	Tetrolet	2DPCA	NTES
W_CRR/%	94.3752	93.5087	94.7578	93.2506	93.3121	95.1582	95.3164	97.1328
Matching time/s	0.1723	0.1081	0.0832	0.0576	0.1321	0.1308	0.1642	0.0672

Table 5. Recognition rate and matching time (IIT Delhi-FK noise database)

Algorithm	Gabor+LDA	PCA	Haar	LBP	Surfacelet	Tetrolet	2DPCA	NTES
W_CRR/%	97.2356	93.2517	96.1798	93.5074	94.8951	96.2135	97.3321	98.0027
Matching time/s	0.1925	0.1125	0.0723	0.0185	0.1723	0.0827	0.1728	0.0572

Table 6. Recognition rate and matching time(HKPU-CFK original database)

Algorithm	Gabor+LDA	PCA	Haar	LBP	Surfacelet	Tetrolet	2DPCA	NTES
W_CRR/%	95.9525	92.5732	95.8512	91.5653	93.7058	95.3675	96.2708	97.1631
Matching time/s	0.2032	0.1324	0.0908	0.0326	0.1872	0.1034	0.1907	0.0625

Table 7. Recognition rate and matching time(HKPU-CFK noise database)

Yuan Wang, Sen Lin. Finger-Knuckle-Print Recognition Based on NSST and Tetrolet Energy Features[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210019

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