Despite claims that finger-vein biometrics can detect aliveness, recent research has shown that current systems can be fooled by forged vein patterns printed on a distinctive paper, raising considerable security concerns regarding the identification authenticity of these systems. Additionally, finger-vein identification exhibits low accuracy rates in real-world applications due to the inferior image quality caused by varied finger thicknesses and vein pattern variations caused by finger axial rotation. To address these issues, we propose a lightweight convolutional neural network (CNN) called the Finger-Vein Recognition and AntiSpoofing Network (FVRAS-Net), which integrates the recognition task and the antispoofing task into a unified CNN model by utilizing a multitask learning (MTL) approach and achieves high security and strong real-time performance. Then, a multi-intensity illumination strategy is introduced into the embedded biometric system to automatically select the most informative image for finger-vein identification, which can effectively improve the recognition performance of the real system. Finally, a challenging finger-vein database with images depicting severe axial finger rotation is built for more rigorous validation of the proposed system, which enriches the database resources for the finger-vein recognition community. Experiments demonstrate that the proposed FVRAS-Net achieves excellent performance in both recognition and antispoofing tasks on public data sets, especially on challenging databases with images depicting axial rotation.