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Finger-vein image enhancement using a fuzzy-based fusion method with Gabor and Retinex filtering.

Shin KY, Park YH, Nguyen DT, Park KR - Sensors (Basel) (2014)

Bottom Line: Our method is novel compared with previous approaches in four respects.Third, the optimal weights required to combine the two Gabor and Retinex filtered images are determined using a defuzzification method.Experimental results using two finger-vein databases showed that the proposed method enhanced the accuracy of finger-vein recognition compared with previous methods.

View Article: PubMed Central - PubMed

Affiliation: Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea. skyandla@dongguk.edu.

ABSTRACT
Because of the advantages of finger-vein recognition systems such as live detection and usage as bio-cryptography systems, they can be used to authenticate individual people. However, images of finger-vein patterns are typically unclear because of light scattering by the skin, optical blurring, and motion blurring, which can degrade the performance of finger-vein recognition systems. In response to these issues, a new enhancement method for finger-vein images is proposed. Our method is novel compared with previous approaches in four respects. First, the local and global features of the vein lines of an input image are amplified using Gabor filters in four directions and Retinex filtering, respectively. Second, the means and standard deviations in the local windows of the images produced after Gabor and Retinex filtering are used as inputs for the fuzzy rule and fuzzy membership function, respectively. Third, the optimal weights required to combine the two Gabor and Retinex filtered images are determined using a defuzzification method. Fourth, the use of a fuzzy-based method means that image enhancement does not require additional training data to determine the optimal weights. Experimental results using two finger-vein databases showed that the proposed method enhanced the accuracy of finger-vein recognition compared with previous methods.

No MeSH data available.


Related in: MedlinePlus

Retinex images obtained using various sigma values with images from database I: (a) the original image with the detected finger boundaries; Retinex images obtained using sigma values of (b) 10, (c) 15, (d) 20, (e) 25, and (f) 50.
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f7-sensors-14-03095: Retinex images obtained using various sigma values with images from database I: (a) the original image with the detected finger boundaries; Retinex images obtained using sigma values of (b) 10, (c) 15, (d) 20, (e) 25, and (f) 50.

Mentions: From Equation (5), we can obtain Equation (6) [23]:(6)logr(x,y)=logL(x,y)−logIc(x,y)The illumination (Ic(x, y)) is assumed to be a convolution of the Gaussian filtering (F(x, y)) and the image(L(x, y)), as shown in Equations (7) and (8) [24]:(7)logr(x,y)=logL(x,y)−log[L(x,y)*F(x,y)](8)F(x,y)=12πσ2e−x2+y22σ2where F(x,y) and logr(x, y) indicate the Gaussian filter and the image produced after Retinex filtering. Retinex images obtained using various sigma values (σ = 10, 15, 20, 25, 50) for Gaussian filtering are shown in Figures 7 and 8. The vein patterns in the images produced after Retinex filtering are more distinct, and the contrast between the vein patterns and the skin regions is higher than that in the original images.


Finger-vein image enhancement using a fuzzy-based fusion method with Gabor and Retinex filtering.

Shin KY, Park YH, Nguyen DT, Park KR - Sensors (Basel) (2014)

Retinex images obtained using various sigma values with images from database I: (a) the original image with the detected finger boundaries; Retinex images obtained using sigma values of (b) 10, (c) 15, (d) 20, (e) 25, and (f) 50.
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f7-sensors-14-03095: Retinex images obtained using various sigma values with images from database I: (a) the original image with the detected finger boundaries; Retinex images obtained using sigma values of (b) 10, (c) 15, (d) 20, (e) 25, and (f) 50.
Mentions: From Equation (5), we can obtain Equation (6) [23]:(6)logr(x,y)=logL(x,y)−logIc(x,y)The illumination (Ic(x, y)) is assumed to be a convolution of the Gaussian filtering (F(x, y)) and the image(L(x, y)), as shown in Equations (7) and (8) [24]:(7)logr(x,y)=logL(x,y)−log[L(x,y)*F(x,y)](8)F(x,y)=12πσ2e−x2+y22σ2where F(x,y) and logr(x, y) indicate the Gaussian filter and the image produced after Retinex filtering. Retinex images obtained using various sigma values (σ = 10, 15, 20, 25, 50) for Gaussian filtering are shown in Figures 7 and 8. The vein patterns in the images produced after Retinex filtering are more distinct, and the contrast between the vein patterns and the skin regions is higher than that in the original images.

Bottom Line: Our method is novel compared with previous approaches in four respects.Third, the optimal weights required to combine the two Gabor and Retinex filtered images are determined using a defuzzification method.Experimental results using two finger-vein databases showed that the proposed method enhanced the accuracy of finger-vein recognition compared with previous methods.

View Article: PubMed Central - PubMed

Affiliation: Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea. skyandla@dongguk.edu.

ABSTRACT
Because of the advantages of finger-vein recognition systems such as live detection and usage as bio-cryptography systems, they can be used to authenticate individual people. However, images of finger-vein patterns are typically unclear because of light scattering by the skin, optical blurring, and motion blurring, which can degrade the performance of finger-vein recognition systems. In response to these issues, a new enhancement method for finger-vein images is proposed. Our method is novel compared with previous approaches in four respects. First, the local and global features of the vein lines of an input image are amplified using Gabor filters in four directions and Retinex filtering, respectively. Second, the means and standard deviations in the local windows of the images produced after Gabor and Retinex filtering are used as inputs for the fuzzy rule and fuzzy membership function, respectively. Third, the optimal weights required to combine the two Gabor and Retinex filtered images are determined using a defuzzification method. Fourth, the use of a fuzzy-based method means that image enhancement does not require additional training data to determine the optimal weights. Experimental results using two finger-vein databases showed that the proposed method enhanced the accuracy of finger-vein recognition compared with previous methods.

No MeSH data available.


Related in: MedlinePlus