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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

Illustration of the defuzzification methods used: (a) FOM, LOM, MOM, and MeOM; and (b) COG.
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f14-sensors-14-03095: Illustration of the defuzzification methods used: (a) FOM, LOM, MOM, and MeOM; and (b) COG.

Mentions: Using these 16 IVs, we can obtain the final optimal weightings based on the defuzzification step. Figure 14 shows an example of defuzzification using the IVs and the membership function for the output value (weight). With each IV, we can obtain the output values (w1, w2, w3, w4, and w5 in Figure 14). Various defuzzification operators are introduced, i.e., the first of maxima (FOM), last of maxima (LOM), middle of maxima (MOM), mean of maxima (MeOM), and center of gravity (COG) [24,26]. In Figure 14a, the FOM method selects the minimum value (w2) among the weight values calculated using the maximum IV (IV1(M) and IV3(H)) as the output weight. The LOM method selects the maximum value (w4) among the weight values calculated using the maximum IV (IV1(M) and IV3(H)) as the output weight. The MOM method selects the middle value ((w2 + w4)/2) among the weight values calculated using the maximum IV (IV1(M) and IV3(H)) as the output weight. Finally, the MeOM method selects the mean value ((w2 + w3 + w4)/3) among the weight values calculated using the maximum IV (IV1(M) and IV3(H)) as the output weight. The output (score) calculated by the COG is w5, as shown in Figure 14b, which is the geometrical center (GC) of the union area of three regions (R1, R2, and R3). Using various defuzzification methods, the output weights are determined for the Gabor filtered image (w in Figure 11) and for the Retinex filtered image (1-w of Figure 11).


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)

Illustration of the defuzzification methods used: (a) FOM, LOM, MOM, and MeOM; and (b) COG.
© Copyright Policy
Related In: Results  -  Collection

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

f14-sensors-14-03095: Illustration of the defuzzification methods used: (a) FOM, LOM, MOM, and MeOM; and (b) COG.
Mentions: Using these 16 IVs, we can obtain the final optimal weightings based on the defuzzification step. Figure 14 shows an example of defuzzification using the IVs and the membership function for the output value (weight). With each IV, we can obtain the output values (w1, w2, w3, w4, and w5 in Figure 14). Various defuzzification operators are introduced, i.e., the first of maxima (FOM), last of maxima (LOM), middle of maxima (MOM), mean of maxima (MeOM), and center of gravity (COG) [24,26]. In Figure 14a, the FOM method selects the minimum value (w2) among the weight values calculated using the maximum IV (IV1(M) and IV3(H)) as the output weight. The LOM method selects the maximum value (w4) among the weight values calculated using the maximum IV (IV1(M) and IV3(H)) as the output weight. The MOM method selects the middle value ((w2 + w4)/2) among the weight values calculated using the maximum IV (IV1(M) and IV3(H)) as the output weight. Finally, the MeOM method selects the mean value ((w2 + w3 + w4)/3) among the weight values calculated using the maximum IV (IV1(M) and IV3(H)) as the output weight. The output (score) calculated by the COG is w5, as shown in Figure 14b, which is the geometrical center (GC) of the union area of three regions (R1, R2, and R3). Using various defuzzification methods, the output weights are determined for the Gabor filtered image (w in Figure 11) and for the Retinex filtered image (1-w of Figure 11).

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