Finger-vein (FV) biometrics is an active and growing topic of research. Most FV systems available today rely on contact sensors that capture vein patterns of a single finger at a time. We have recently completed a project aimed at designing a contactless vein sensing platform, named sweet. In this paper we present a new FV dataset collected using sweet. The dataset includes multiple FV samples from 120 subjects and 280 presentation attack instruments (PAI), captured in a contactless manner. Further, we present baseline FV authentication (FVA) results achieved for proposed dataset. The sweet platform is equipped to capture a sequence of images suitable for photometric-stereo (PS) reconstruction of 3D surfaces. We present a FV presentation attack detection (PAD) method based on PS reconstruction, and the corresponding baseline FV PAD results on the proposed dataset. (See Footnote 4.)