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Title
Abstract
Figures
Introduction
Experimental Results
Results for Proposed Feature Extraction Method
Discussions
Conclusions
References
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Engineering
Environmental Engineering

Palmprint verification based on principal lines

Profile image of Wei JiaWei Jia

2008, Pattern Recognition

https://doi.org/10.1016/J.PATCOG.2007.08.016
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13 pages

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Abstract

In this paper, we propose a novel palmprint verification approach based on principal lines. In feature extraction stage, the modified finite Radon transform is proposed, which can extract principal lines effectively and efficiently even in the case that the palmprint images contain many long and strong wrinkles. In matching stage, a matching algorithm based on pixel-to-area comparison is devised to calculate the similarity between two palmprints, which has shown good robustness for slight rotations and translations of palmprints. The experimental results for the verification on Hong Kong Polytechnic University Palmprint Database show that the discriminability of principal lines is also strong. ᭧

Figures (21)
Fig. 1. Three typical palmprint images.
Fig. 1. Three typical palmprint images.
Fig. 2 shows two examples of the MFRAT whose sizes are 7x 7 and 14 x 14, respectively, and whose lines, L(0;), are at directions of 2/12, 27/12,..., and 62/12, respectively. Due to space limitation, the remaining lines L(0;) at directions of 71/12, 82/12,..., and 127/12, are not depicted here. If the line in the MFRAT is 1 pixel wide, p should be an odd num- ber in order to clearly define the center of Te In 14 x 14 MEFRAT, the center area of Zz; contains four pixels, thus the di- rections and the energies of these four pixels could be calculated In this way, the directions and energies of all pixels are cal- culated if the center of lattice Zz move over an image pixel by pixel (or pixels by pixels). For an image / (x, y) of size m xn, if the values of all pixels are replaced by their directions and en- ergies, two new images, Direction_image and Energy_image, can be created, respectively, i.e.
Fig. 2 shows two examples of the MFRAT whose sizes are 7x 7 and 14 x 14, respectively, and whose lines, L(0;), are at directions of 2/12, 27/12,..., and 62/12, respectively. Due to space limitation, the remaining lines L(0;) at directions of 71/12, 82/12,..., and 127/12, are not depicted here. If the line in the MFRAT is 1 pixel wide, p should be an odd num- ber in order to clearly define the center of Te In 14 x 14 MEFRAT, the center area of Zz; contains four pixels, thus the di- rections and the energies of these four pixels could be calculated In this way, the directions and energies of all pixels are cal- culated if the center of lattice Zz move over an image pixel by pixel (or pixels by pixels). For an image / (x, y) of size m xn, if the values of all pixels are replaced by their directions and en- ergies, two new images, Direction_image and Energy_image, can be created, respectively, i.e.
Fig. 2. The 7 x 7 and the 14 x 14 MFRAT. (a) and (g) 0; = 2/12; (b) and (h) 02 = 27/12; (c) and (i) 03 = 32/12; (d) and (j) 04 = 47/12; (e) and (k) 05 = 5n/12; (f) and (1) 0 = 6/12.
Fig. 2. The 7 x 7 and the 14 x 14 MFRAT. (a) and (g) 0; = 2/12; (b) and (h) 02 = 27/12; (c) and (i) 03 = 32/12; (d) and (j) 04 = 47/12; (e) and (k) 05 = 5n/12; (f) and (1) 0 = 6/12.
Fig. 3. Images appearing in feature extraction stage. (a) Original image; (b) Energy_image; (c) Lines_image; (d) LA_image; (e) LB_image; (f) R(LA_image, (g) R(LB_image); (h) thinning lines of (d).
Fig. 3. Images appearing in feature extraction stage. (a) Original image; (b) Energy_image; (c) Lines_image; (d) LA_image; (e) LB_image; (f) R(LA_image, (g) R(LB_image); (h) thinning lines of (d).
Fig. 5. Principal lines extracted from the same palm, but different images. Fig. 4. Pixel-to-pixel comparison and pixel-to-area comparison.
Fig. 5. Principal lines extracted from the same palm, but different images. Fig. 4. Pixel-to-pixel comparison and pixel-to-area comparison.
Fig. 6. Extracting principal lines from two different palmprints.
Fig. 6. Extracting principal lines from two different palmprints.
Fig. 7. Extracting principal lines using different parameters. (a) M =500, p= 14, N= 12, and W =2; (b) M= 1800, p= 14, N=12, and W =2; (c) p=7, N=12, W=1, and M = 1000; (d) p= 22, N= 12, W =2, and M = 1000.
Fig. 7. Extracting principal lines using different parameters. (a) M =500, p= 14, N= 12, and W =2; (b) M= 1800, p= 14, N=12, and W =2; (c) p=7, N=12, W=1, and M = 1000; (d) p= 22, N= 12, W =2, and M = 1000.
‘ig. 8. The directional energies of lines detected from Figs. 3(a) and 6(c) by Gabor filter with different parameters. (a) and (b) u = 0.2, o =3.5, and the size f filter is 20 x 20; (c) and (d) u = 0.0916, g = 5.6179, and the size of filter is 35 x 35.
‘ig. 8. The directional energies of lines detected from Figs. 3(a) and 6(c) by Gabor filter with different parameters. (a) and (b) u = 0.2, o =3.5, and the size f filter is 20 x 20; (c) and (d) u = 0.0916, g = 5.6179, and the size of filter is 35 x 35.
Fig. 9. The directional energies of lines detected from Figs. 3(a) and 6(c) by using 9 x 9 and 17 x 17 MFRAT. (a) and (b) p=9, N = 12, and W = 1; (c) and (d) p=17, N=12, and W=1.
Fig. 9. The directional energies of lines detected from Figs. 3(a) and 6(c) by using 9 x 9 and 17 x 17 MFRAT. (a) and (b) p=9, N = 12, and W = 1; (c) and (d) p=17, N=12, and W=1.
Fig. 10. Enhanced palmprint image and extracted palm lines using 9 x 9 MFRAT. (a) and (b) Enhanced palmprint image; (c) and (d) extracted palm lines from (a) and (b).
Fig. 10. Enhanced palmprint image and extracted palm lines using 9 x 9 MFRAT. (a) and (b) Enhanced palmprint image; (c) and (d) extracted palm lines from (a) and (b).
Fig. 12. Palm lines detected from Figs. 3(a) and 6(c) using Wu’s approach. Fig. 11. Palm lines detected from Figs. 3(a) and 6(c) using Liu’s approach.
Fig. 12. Palm lines detected from Figs. 3(a) and 6(c) using Wu’s approach. Fig. 11. Palm lines detected from Figs. 3(a) and 6(c) using Liu’s approach.
Fig. 13. Comparing Wu’s approach with proposed approach. (a) Original palmprint image containing wide principal lines. (b) Magnitude of second derivativ detected from (a) by using Wu’s approach. (c) Directional energies detected from (a) by using 9 x 9 MFRAT (N = 12 and W = 1). (d) Directional energie detected from (a) by using 17 x 17 MFRAT (N = 12, and W = 1).
Fig. 13. Comparing Wu’s approach with proposed approach. (a) Original palmprint image containing wide principal lines. (b) Magnitude of second derivativ detected from (a) by using Wu’s approach. (c) Directional energies detected from (a) by using 9 x 9 MFRAT (N = 12 and W = 1). (d) Directional energie detected from (a) by using 17 x 17 MFRAT (N = 12, and W = 1).
Fig. 14. Experimental results of verification. (a) and (b) Genuine and imposter distributions of matching scores for Databases I and II. (c) and (d) FAR and FRR curves for Databases I and II.
Fig. 14. Experimental results of verification. (a) and (b) Genuine and imposter distributions of matching scores for Databases I and II. (c) and (d) FAR and FRR curves for Databases I and II.
Experimental results near cross-over points (boldface) in Fig. 14 Table 1
Experimental results near cross-over points (boldface) in Fig. 14 Table 1
Fig. 16. The principal lines from two people with similar structure, but witl dissimilar position. Fig. 15. ROC curves of the proposed approach, Palmcode, Wu’s approach and Liu’s approach.
Fig. 16. The principal lines from two people with similar structure, but witl dissimilar position. Fig. 15. ROC curves of the proposed approach, Palmcode, Wu’s approach and Liu’s approach.
Fig. 17. The principal lines from two people with similar structure anc position.
Fig. 17. The principal lines from two people with similar structure anc position.
Matching scores among different palmprint images in Fig. 18 Table 2
Matching scores among different palmprint images in Fig. 18 Table 2
Fig. 18. Eight palmprint images from different people, which have similar principal lines.
Fig. 18. Eight palmprint images from different people, which have similar principal lines.

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  25. About the Author-DE-SHUANG HUANG received the B.Sc. degree in electronic engineering from the Institute of Electronic Engineering, Hefei, China, in 1986, the M.Sc. degree in electronic engineering from the National Defense University of Science and Technology, Changsha, China, in 1989, and the Ph.D. degree in electronic engineering from Xidian University, Xian, China, in 1993. From 1993 to 1997, he was a Postdoctoral Student at the Beijing Institute of Technology, Beijing, China, and the National Key Laboratory of Pattern Recognition, Chinese Academy of Sciences (CAS), Beijing. In 2000, he was a professor, and joined the Institute of Intelligent Machines, CAS, as a member of the Hundred Talents Program of CAS. He had published over 190 papers and,

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    Kuo-chin Fan

    Sensors, 2015

    In this paper, we present a reliable and robust biometric verification method based on bimodal physiological characteristics of palms, including the palmprint and palm-dorsum vein patterns. The proposed method consists of five steps: (1) automatically aligning and cropping the same region of interest from different palm or palm-dorsum images; (2) applying the digital wavelet transform and inverse wavelet transform to fuse palmprint and vein pattern images; (3) extracting the line-like features (LLFs) from the fused image; (4) obtaining multiresolution representations of the LLFs by using a multiresolution filter; and (5) using a support vector machine to verify the multiresolution representations of the LLFs. The proposed method possesses four advantages: first, both modal images are captured in peg-free scenarios to improve the user-friendliness of the verification device. Second, palmprint and vein pattern images are captured using a low-resolution digital scanner and infrared (IR) camera. The use of low-resolution images results in a smaller database. In addition, the vein pattern images are captured through the invisible IR spectrum, which improves antispoofing. Third, since the physiological characteristics of palmprint and vein pattern images are different, a hybrid fusing rule can be introduced to fuse the decomposition coefficients of different bands. The proposed method fuses decomposition coefficients at different decomposed levels, with different image sizes, captured from different sensor devices. Finally, the proposed method operates automatically and hence no parameters need to be set manually. Three thousand palmprint images and 3000 vein pattern images were collected from 100 volunteers to verify the validity of the proposed method. The results show a false rejection rate of 1.20% and a false acceptance rate of 1.56%. It demonstrates the validity and excellent performance of our proposed method comparing to other methods.

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