  This work proposes a novel process of using pen tip and tail 3D trajectory
for air signature. To acquire the trajectories we developed a new pen tool and
a stereo camera was used. We proposed SliT-CNN, a novel 2D spatial-temporal
convolutional neural network (CNN) for better featuring of the air signature.
In addition, we also collected an air signature dataset from $45$ signers.
Skilled forgery signatures per user are also collected. A detailed benchmarking
of the proposed dataset using existing techniques and proposed CNN on existing
and proposed dataset exhibit the effectiveness of our methodology.
