# Individuals Identification Based on Palm Vein Matching under a Parallel Environment
## 3 Methodology
### Pre-Processing of Finger-Vein Images  
Palm vein images are normally captured by using an NIR light. Usually, in contactless systems, the captured image contains the whole hand including fingers. Moreover and much more significant, the captured vein images present a low contrast due to NIR acquisition process. Hence, pre-processing of palm vein images is a very important operation as the initialization step for feature extraction and matching procedures. Regarding this, the image enhancement procedures, as well as ROI segmentation, are crucial to extracting the latent information of palm vein patterns. The pre-processing procedure in our method is composed of three sub-processes with the aim to obtain an enhanced image: ROI segmentation, intensity normalization, and histogram equalization, which are exemplified in Figure 5.  
ROI of a palm vein image refers to the region of palm which reserves the most useful information for the recognition process. Thus, one essential rule in the extraction of ROI is that it should have sufficient vein features for extraction and matching [58]. Furthermore, in contactless imaging, it should be noted that palm vein images present a lot of translational and rotational variations. Therefore, in our approach, we applied the ROI extraction method proposed by [PERSON] and [PERSON] [28], which is suitable for contactless palm vein recognition. The process involves various pre-processing steps, such as background segmentation, image rotation, and ROI extraction and normalization, making the feature extraction and matching processes much more robust to axis changes and variations. Figure 4: Flowchart of our approach for palm vein recognition based on deformation information. The modifications and/or contributions with respect to [45] are highlighted in bold text. First, the acquired images are binarized to differentiate the palm and the background region by a modified OTSU algorithm [59], as shown in Figure 5b. Next, taking the middle point of the wrist cut line as reference point (\(P_{ref}\)), the radial distance function (RDF) in-between contour points is calculated to estimate five fingertips (local maximums) and four finger-webs (local minimums), which can be taken as landmarks for image rotation and ROI extraction. Then, the points \(P_{1}\), which corresponds to the the midpoint of the two valley points on both sides of the index finger, and \(P_{2}\), that similarly corresponds to the midpoint of the valley points on both sides of the little finger, are selected as datum points, as shown in Figure 5c. This method is better to other approaches [26; 53; 60] which use the valley point between the small finger and ring finger, and the valley point between the middle finger and the index finger as reference points, and in that way they omit an information-rich region at the edge of the palm. Thus, taking the straight line \(\overline{P_{1}P_{2}}\) formed by the points \(P_{1}\) and \(P_{2}\), since \(\theta\) represents the angle between \(\overline{P_{1}P_{2}}\) and the horizontal line \(L\) (Figure 5d), let \(d\) denote the distance between \(P_{1}\) and \(P_{2}\), and let \(a\) be the scaling factor. Hence, we obtain the following relationships as in [28]:  
\[\theta =\tan^{-1}(x_{P_{2}}-x_{P_{1}})/(y_{P_{2}}-y_{P_{1}}), \tag{1}\] \[d =\sqrt{(x_{P_{2}}-x_{P_{1}})^{2}+(y_{P_{2}}-y_{P_{1}})^{2}},\] (2) \[a=d/c, \tag{3}\]  
where \((x_{P_{1}},y_{P_{1}})\) and \((x_{P_{2}},y_{P_{2}})\) are the coordinates of points \(P_{1}\) and \(P_{2}\), respectively, and \(c\) is the side length of the normalized ROI, which is fixed in 192 in order to reduce the processing time and to standardize the size of palm vein images for the matching process. Consequently, to eliminate the influences of palm rotation, the palm image is rotated an angle \(\theta\).