# Individuals Identification Based on Palm Vein Matching under a Parallel Environment
## 3 Methodology
### Pre-Processing of Finger-Vein Images
Later, distance \(d\) is computed between the rotated points  
Figure 5: Examples of images obtained by applying sub-processes of pre-processing procedure for ROI segmentation, intensity normalization and histogram equalization: (**a**) original image; (**b**) background segmentation; (**c**) description of \(P_{ref}\), five fingertips and four finger-webs, \(P_{1}\) and \(P_{2}\); (**d**) the angle \(\theta\) between \(\overline{P_{1}P_{2}}\) and the horizontal line \(L\); (**e**) segmented ROI from rotated palm image; (**f**) normalized palm vein ROI; (**g**) uniform intensity ROI image; and (**h**) final enhanced palm vein ROI after CLAHE. and \(P_{2}^{\prime}\) and a square region of side length \(d\) is selected to define the palm vein ROI, as shown in Figure 5e. Finally, we perform scaling of ROI by \(a\) factor and the normalized ROI is obtained; Figure 5f shows an example of the segmented ROI. The intensity normalization process is carried out in the pre-processing step aiming to solve another problem that occurs during the image acquisition procedure, which is related to lighting conditions. The images have different intensity ranges due to the difference in illumination and hand position. The normalization process lowers the pixel gray value variation along the vein patterns and normalizes the pixel intensity distribution. Instead of the well-known global normalization used by different authors [58; 61; 62], we propose applying the block-local normalization introduced by [PERSON] and [PERSON] [53] to ensure common range intensities between 0 and 255 in all ROI segmented images. Local normalization considers the average of the pixels of the local area and not the entire image as with global normalization. In addition, the local or block-local normalization is more appropriate than global normalization because of the varying of gray in different areas of the image. As in [53], the palm vein image is divided into slightly overlapping \(32\times 32\) blocks (three pixels overlapping between two blocks to address the blocky effect), and their mean values constitute the coarse estimates of background illumination for individual blocks. The uniform intensity image is achieved by subtracting the estimated background illumination from each original template; Figure 5g illustrates an example of the resulted image. Once the intensity normalization process takes place, as the final pre-processing step, the contrast of the ROI image is enhanced by using the Contrast Limited Adaptive Histogram Equalization (CLAHE) [63] in which contrast enhancement is carried out in a local neighborhood. This approach is different to some authors that use histogram equalization to globally enhance the contrast in palm vein images [40; 41; 53]. Thereby, the pre-processing procedure enhances fine details of vein patterns that have been blurred as a natural effect of NIR image acquisition, as shown in Figure 5h. Moreover, the CLAHE operator prevents over amplification of noise in relatively homogeneous regions of the image, as it computes several histograms, each corresponding to a section of the image, and uses them to redistribute the values locally on a predefined neighborhood. In our approach, the size of the neighborhood is empirically fixed to be a \(15\times 15\) sized window and the contrast is limited to 255.