Limits...
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

Illustrations showing the linear membership outputs based on four input values: (a) μ1, (b) std1, (c) μ2, and (d) std2.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3958271&req=5

f13-sensors-14-03095: Illustrations showing the linear membership outputs based on four input values: (a) μ1, (b) std1, (c) μ2, and (d) std2.

Mentions: Using the four input values (μ1, std1, μ2, and std2) obtained in the local window, the eight corresponding output values are calculated as f1(L) and f1(H) for μ1, f2(L) and f2(H) for std1, f3(L) and f3(H) for μ2, and f4 (L) and f4 (H) for std2 using four linear membership functions, as shown in Figure 13, where, f1(˙), f2(˙), f3(˙), and f4(˙) are the membership functions that correspond to μ1, std1, μ2, and std2, respectively. Therefore, 16 combination pairs of the above output values are obtained as {(f1(L), f2(L), f3(L), f4 (L)), (f1(L), f2(L), f3(L), f4 (H)), (f1(L), f2(L), f3(H), f4 (L)), (f1(L), f2(L), f3(H), f4 (H)),…(f1(H), f2(H), f3(H), f4 (H))}. Assuming that the values of f1(L), f1(H), f2(L), f2(H), f3(L), f3(H), f4(L), and f4(H) are 0.39, 0.61, 0.55, 0.45, 0.67, 0.33, 0.27, and 0.73, respectively, we can obtain the values listed in Table 3 based on the values in Table 2.


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)

Illustrations showing the linear membership outputs based on four input values: (a) μ1, (b) std1, (c) μ2, and (d) std2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958271&req=5

f13-sensors-14-03095: Illustrations showing the linear membership outputs based on four input values: (a) μ1, (b) std1, (c) μ2, and (d) std2.
Mentions: Using the four input values (μ1, std1, μ2, and std2) obtained in the local window, the eight corresponding output values are calculated as f1(L) and f1(H) for μ1, f2(L) and f2(H) for std1, f3(L) and f3(H) for μ2, and f4 (L) and f4 (H) for std2 using four linear membership functions, as shown in Figure 13, where, f1(˙), f2(˙), f3(˙), and f4(˙) are the membership functions that correspond to μ1, std1, μ2, and std2, respectively. Therefore, 16 combination pairs of the above output values are obtained as {(f1(L), f2(L), f3(L), f4 (L)), (f1(L), f2(L), f3(L), f4 (H)), (f1(L), f2(L), f3(H), f4 (L)), (f1(L), f2(L), f3(H), f4 (H)),…(f1(H), f2(H), f3(H), f4 (H))}. Assuming that the values of f1(L), f1(H), f2(L), f2(H), f3(L), f3(H), f4(L), and f4(H) are 0.39, 0.61, 0.55, 0.45, 0.67, 0.33, 0.27, and 0.73, respectively, we can obtain the values listed in Table 3 based on the values in Table 2.

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