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

Examples of finger region detection using images from database I: (a) original images and (b) detection results for the finger boundaries.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-14-03095: Examples of finger region detection using images from database I: (a) original images and (b) detection results for the finger boundaries.

Mentions: The y-positions indicate where the maximum values for template matching are obtained (at each x-position) using the detection masks shown in Figure 2, which are considered the upper and lower edge boundaries [6,17,20]. A thick finger area (for example, the left part of Figure 3a) usually lacks a vein pattern because the NIR light has difficulty penetrating the thick finger to be captured by the camera. In addition, the thin vein pattern information is not visible in the fingertip region of a captured finger-vein image. To consider these conditions, we define the left (X1) and right (X2) boundaries in the horizontal direction, as shown in Figures 3a and 4a. The values of X1 and X2 were defined empirically based on the characteristics of the finger-vein database used in the experiments. In database I, with a 640 × 480 image size [6,17,20], the values of X1 and X2 are 220 and 169, respectively. In database II, with a 320 × 240 image size [21], the values of X1 and X2 are set to 20 and 51, respectively (detailed explanations of databases I and II are provided in Section 3). The values of X1 and X2 are larger in database I than those in database II for the following reasons. Database I was collected using a device produced in our laboratory. The device includes a hole where the finger that needs to be recognized is placed. The hole is small but a small area of the finger, i.e., the region between the 1st and 2nd knuckles, can be observed through the hole by the camera in the device. Therefore, the unseen (dark) areas from the left and right boundaries of the image are larger in database I than those in the database II, as shown in Figures 3 and 4. Consequently, we used the larger values for X1 and X2 in database I. Figures 3 and 4 show examples of finger region detection using detection masks. Because of the noise in the upper and lower boundaries of the finger region of database II, the region of interest used for finger-vein recognition is reduced in the vertical direction compared with the detected finger region, as shown in Figure 4.


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)

Examples of finger region detection using images from database I: (a) original images and (b) detection results for the finger boundaries.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-14-03095: Examples of finger region detection using images from database I: (a) original images and (b) detection results for the finger boundaries.
Mentions: The y-positions indicate where the maximum values for template matching are obtained (at each x-position) using the detection masks shown in Figure 2, which are considered the upper and lower edge boundaries [6,17,20]. A thick finger area (for example, the left part of Figure 3a) usually lacks a vein pattern because the NIR light has difficulty penetrating the thick finger to be captured by the camera. In addition, the thin vein pattern information is not visible in the fingertip region of a captured finger-vein image. To consider these conditions, we define the left (X1) and right (X2) boundaries in the horizontal direction, as shown in Figures 3a and 4a. The values of X1 and X2 were defined empirically based on the characteristics of the finger-vein database used in the experiments. In database I, with a 640 × 480 image size [6,17,20], the values of X1 and X2 are 220 and 169, respectively. In database II, with a 320 × 240 image size [21], the values of X1 and X2 are set to 20 and 51, respectively (detailed explanations of databases I and II are provided in Section 3). The values of X1 and X2 are larger in database I than those in database II for the following reasons. Database I was collected using a device produced in our laboratory. The device includes a hole where the finger that needs to be recognized is placed. The hole is small but a small area of the finger, i.e., the region between the 1st and 2nd knuckles, can be observed through the hole by the camera in the device. Therefore, the unseen (dark) areas from the left and right boundaries of the image are larger in database I than those in the database II, as shown in Figures 3 and 4. Consequently, we used the larger values for X1 and X2 in database I. Figures 3 and 4 show examples of finger region detection using detection masks. Because of the noise in the upper and lower boundaries of the finger region of database II, the region of interest used for finger-vein recognition is reduced in the vertical direction compared with the detected finger region, as shown in Figure 4.

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