Laser & Optoelectronics Progress, Volume. 58, Issue 2, 0210019(2021)
Finger-Knuckle-Print Recognition Based on NSST and Tetrolet Energy Features
Fig. 1. Five basic Tetrominoes
Fig. 2. Flow chart of our method
Fig. 3. Result processed by our method. (a) Image processed by our method; (b) energy characteristic surface
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
Fig. 5. Example of HKPU-FKP database ROI. (a) Original database; (b) noise database
Fig. 6. Histogram equalization contrast. (a) Image before equalization; (b) histogram before equalization; (c) image after equalization; (d) histogram after equalization
Fig. 7. Matching curve and ROC (HKPU-FKP and noise database). (a) Matching curve;(b) ROC
Fig. 8. Example of IIT Delhi-FK database. (a) Original database; (b) noise database
Fig. 9. Matching curve and ROC (IIT Delhi-FK and noise database). (a) Matching curve; (b) ROC
Fig. 10. Example of HKPU-CFK ROI database. (a) Original database; (b) noise database
Fig. 11. Matching curve and ROC (HKPU-CFK and noise database). (a) Matching curve; (b) ROC
Fig. 12. Correct recognition rate and matching time. (a) Correct recognition rate; (b) matching time
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Yuan Wang, Sen Lin. Finger-Knuckle-Print Recognition Based on NSST and Tetrolet Energy Features[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210019
Category: Image Processing
Received: Jun. 17, 2020
Accepted: Jul. 20, 2020
Published Online: Jan. 11, 2021
The Author Email: Wang Yuan (826998554@qq.com)