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Scattering removal for finger-vein image restoration.

Yang J, Zhang B, Shi Y - Sensors (Basel) (2012)

Bottom Line: To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues.Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration.Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

View Article: PubMed Central - PubMed

Affiliation: Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China. jfyang@cauc.edu.cn

ABSTRACT
Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the intrinsic factors causing finger-vein image degradation, and then propose a simple but effective image restoration method based on scattering removal. To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration. Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

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POC measure. Left: r(x, y) of two same finger-vein images. Right: r(x, y) of two finger-vein images from different classes.
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f8-sensors-12-03627: POC measure. Left: r(x, y) of two same finger-vein images. Right: r(x, y) of two finger-vein images from different classes.

Mentions: In this section, the Phase-Only-Correlation (POC) measure proposed in [27] is simply used for handling the finger-vein matching problem based on the restored finger-vein images. Assume that I0i (x, y) and I0j (x, y) are two restored images, and Fi(u, v) and Fj(u, v) represent their 2D DFT, respectively, according to the property of Fourier transform, that is,(10)I0i (x,y)∘I0j (x,y)⇔Fi(u,v)Fj (u,v),¯where “ ○ ” denotes a 2D correlation operator, we can compute the cross phase spectrum as(11)R(u,v)=Fi(u,v)Fj(u,v)¯‖Fi(u,v)Fj(u,v)¯‖=ej^θ(u,v).Let r(x, y) = IDFT(R(u, v)), thus, r(x, y) is called a POC measure. The POC measure has a sharp peak when two restored finger-vein images are similar, whereas it will be near zero for those from different classes, as shown in Figure 8. Moreover, POC measure is insensitive to image shifts and noises in practice.


Scattering removal for finger-vein image restoration.

Yang J, Zhang B, Shi Y - Sensors (Basel) (2012)

POC measure. Left: r(x, y) of two same finger-vein images. Right: r(x, y) of two finger-vein images from different classes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376557&req=5

f8-sensors-12-03627: POC measure. Left: r(x, y) of two same finger-vein images. Right: r(x, y) of two finger-vein images from different classes.
Mentions: In this section, the Phase-Only-Correlation (POC) measure proposed in [27] is simply used for handling the finger-vein matching problem based on the restored finger-vein images. Assume that I0i (x, y) and I0j (x, y) are two restored images, and Fi(u, v) and Fj(u, v) represent their 2D DFT, respectively, according to the property of Fourier transform, that is,(10)I0i (x,y)∘I0j (x,y)⇔Fi(u,v)Fj (u,v),¯where “ ○ ” denotes a 2D correlation operator, we can compute the cross phase spectrum as(11)R(u,v)=Fi(u,v)Fj(u,v)¯‖Fi(u,v)Fj(u,v)¯‖=ej^θ(u,v).Let r(x, y) = IDFT(R(u, v)), thus, r(x, y) is called a POC measure. The POC measure has a sharp peak when two restored finger-vein images are similar, whereas it will be near zero for those from different classes, as shown in Figure 8. Moreover, POC measure is insensitive to image shifts and noises in practice.

Bottom Line: To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues.Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration.Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

View Article: PubMed Central - PubMed

Affiliation: Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China. jfyang@cauc.edu.cn

ABSTRACT
Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the intrinsic factors causing finger-vein image degradation, and then propose a simple but effective image restoration method based on scattering removal. To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration. Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

Show MeSH