Laser & Optoelectronics Progress, Volume. 58, Issue 2, 0210019(2021)
Finger-Knuckle-Print Recognition Based on NSST and Tetrolet Energy Features
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
|
|
|
|
|
|
|
Get Citation
Copy Citation Text
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: Yuan Wang (826998554@qq.com)