# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
## 3 Attention-Gated Residual U-Net and ECA-ResNet
### Region of Interest Extraction  
Similarly to the face recognition system, only some of the information acquired by the near-infrared radiation (NIR) camera is necessary, and region of interest (ROI) extraction or detection is an essential aspect of improving the model performance. This step also improves the palm vein recognition system in terms of computational efficiency by reducing the template size. Despite having advantages such as better hygiene and a user-friendly approach, contactless systems produce inconsistent results, including hand displacement, rotation, and zooming at different degrees. Therefore, this study uses a reliable background subtraction and ROI extraction process as the preprocessing method to overcome these problems. The segmentation process involves five steps: segmenting the hand from the background using the binarization method, locating hand contours, centroid positioning and detecting key points, normalization, and ROI extraction. First, this study used Gaussian blur to remove the image noise caused by external light factors. The contactless palm images obtained generally contain the entire hand with a darker background due to the variations in NIR light response. Therefore, the hand contour information can be segmented using the Otsu binarization method. Occasionally, due to the influence of lighting conditions--such as the brightness of some areas of the background--being similar to that of the segmented hand, the output of the Otsu binarization process may contain a couple of smaller, irregularly shaped areas that are also segmented besides the segmented hand. These areas can introduce noise and compromise the accuracy of the subsequent process. Thus, the morphological operations such as erosion and dilation are utilized to eliminate these irregular areas and obtain better segmentation results. After obtaining hand contours from binarization, the point \(C\) is positioned in the center to compute the radial distance function (RDF). The process of RDF is performed by calculating the distance between each point on the contour of the hand to the centroid \(C\), which is a measure of the radial distance for each point. Once the radial distances for all points on the contour is calculated, the maxima and minima points can be determined. The maxima typically correspond to the fingertips, as these are the points furthest away from \(C\). The minima, on the other hand, correspond to the finger valleys, as these are the points closest to \(C\). This can be achieved by finding the local maxima and minima in the RDF curve, as shown in Figure 2. From the obtained five maxima finger tips, the thumb, which is identified as the fingertip with the smallest radial distance from the \(C\), is excluded to simplify the process. Finally, four maxima fingertips and three corresponding minima finger valleys are obtained, as shown in Figure 3c. From these points, the leftmost and rightmost valley points can be, respectively, defined as \(P1\) and \(P2\), respectively. Because the normalization of the hand position is a fundamental step for identification, we rotationally normalized the images by horizontally aligning them. The normalizing angle \(\theta\) was then  
Figure 1: Overall proposed method for palm vein authentication. computed as the angle between \(d\), which is shown in Equation (1), and the dotted red line, as shown in Figure 3d. The hand images are then zoomed and rotated according to \(\theta\) in Equation (2) by using bilinear interpolation, as depicted in Figure 3e.