Finger-vein biometrics has recently gained significant attention due to its robust privacy and high security features. Despite notable advancements, most existing methods focus on extracting features from a 2-dimensional (2D) image projected from 3D vein vessels with a single view. However, recognition based on a single view is prone to errors due to variations in finger positioning, especially those caused by finger roll movements, which can degrade recognition performance. To address this challenge, we propose ABLSTM-TSVT, an Attention Bidirectional LSTM-based Temporal-Spatial Vein Transformer for multi-view finger-vein recognition. First, we enhance LSTM with an attention mechanism to create an attention LSTM for extracting temporal features. We further improve this by introducing a local attention module, which learns temporal dependencies between a patch (token) and its adjacent patches across multiple views, integrating it with the attention LSTM to form a temporal attention module. Second, we develop a spatial attention module that captures the spatial dependencies of patches within an image. Finally, merging the temporal and the spatial attention modules, we create our temporal-spatial transformer model, which effectively represents features from multi-view images. Experimental results on two multi-view datasets demonstrate that our approach outperforms state-of-the-art approaches in enhancing identification accuracy and reducing verification errors in vein classifiers.

A small-scale jumping robot can reach obstacles much larger than its size. It is important for a jumping robot to perform intermittent jumps to cross through rough terrains. However, the limitations of conventional structures hinder the further integration of functions to a miniature (sub-50 g) jumping robot. No sub-50 g jumpers could perform intermittent jumps with adjustable jumping trajectories. In this work, we proposed an origami-based miniature jumper, which performed intermittent jumps with adjustable omni-directional trajectories. The intermittent jumps were achieved by the jumping and self-righting mechanisms, which were actuated by a single motor. The clockwise and counterclockwise rotation of the motor actuated the loading, self-righting and triggering process, respectively. The jumping height was adjustable by adjusting the rotation angle of the motor. Meanwhile, the take-off pitch & yaw angle adjustment methods were integrated into the robot. Therefore, we demonstrated a 9 cm, 13.5 g prototype with functions of re-loading, self-righting, jumping height adjustment and take-off pitch & yaw angle adjustment. The robot could adjust jumping height from 16 to 34 cm and self-right for the next jump. The results revealed that our robot could jump across different obstacles with different scales and directions. The mobility was greatly increased compared with other miniature jumping robots.