# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
## 1 Introduction  
Biometric identification is an authentication process leveraging unique physical or behavioral human features. During the COVID-19 pandemic, various researchers showed substantially increased interest in contactless biometric identification over contact-based biometrics, such as fingerprints [1]; this is because, in contact-based methods, users need to put their fingers directly on a sensor, which is not practical for health reasons. Contactless biometric systems such as those using the iris [2], face [3], or palm print [4, 5], are considered to be better functional recognition systems for this reason. However, these extrinsic biometrics features are susceptible to spoofing and can be significantly impacted by various factors such as the age and health of the person and the condition of the skin or injuries. On the other hand, a contactless biometrics feature such as palm vein is an intrinsic biometrics trait and offers several advantages: first, it provides better privacy and security advantages, as obtaining the palm vein is only possible using special equipment, thus preventing forging. Second, palm vein patterns remain stable throughout an individual's lifespan and disappear in the absence of blood flow. Third, human palms include highly complicated and unique vein patterns, as the structure of the pattern differs even betweenidentical twins. Moreover, the collection process of contactless palm images is comfortable, easily accepted by users, and provides better hygiene, as there is no need for any interaction between the user's hand and a sensor on a public device. This complexity and consistency make palm veins an exceptionally reliable biometric feature for personal identification, surpassing other external features. Palm veins are often collected via the reflection method. In this approach, near-infrared light is transmitted from the sensor through the person's hand. Because the hemoglobin in a vein can absorb more near-infrared radiation passing through a hand than the surrounding tissues, the palm vein patterns appear dark. This reflective approach allows for contactless pattern recognition and user identification. In conclusion, palm vein authentication is considered to be more secure and practical due to privacy concerns, and it, therefore, offers excellent research potential and broad application prospects. Recently proposed approaches for palm vein recognition generally face the problem of a quality issue in the data collection stage. The acquisition of contactless palm images using near-infrared light often results in poor contrast between the vein pixels and non-vein areas. The visibility of the palm vein can be compromised by various factors, including surrounding temperature, lighting conditions, and illumination. This causes noise and optical blurring in the palm images, which results in degraded recognition accuracy. Enhancing or segmenting the accurate vein patterns is a crucial aspect of improving the robustness of palm vein features; however, it is difficult to extract blood vessels and effectively remove the optical blurring. Primarily, this makes it more challenging to extract the precise vein pattern using distribution assumption-based hand-crafted methods [6; 7]. Moreover, the contactless palm vein biometrics system suffers from the problem of image rotation, position translation, and scale transformation. Last but not least, most of the deep-learning-based palm vein recognition studies have to this point focused on a classification approach using the SoftMax loss, a popular loss function for recognition tasks. Although SoftMax loss offers excellent performance for inter-class separation, it is ineffective in minimizing intra-class diversity, which decreases the discriminative features [8; 9]. The attention-gated residual U-Net addresses the first problem. The U-Net-based segmentation model segments the near-accurate palm vein vessels from the original grayscale images. The segmentation model filters out the irrelevant and noisy pixels for recognition.