  Eye blinking detection in the wild plays an essential role in deception
detection, driving fatigue detection, etc. Despite the fact that numerous
attempts have already been made, the majority of them have encountered
difficulties, such as the derived eye images having different resolutions as
the distance between the face and the camera changes; or the requirement of a
lightweight detection model to obtain a short inference time in order to
perform in real-time. In this research, two problems are addressed: how the eye
blinking detection model can learn efficiently from different resolutions of
eye pictures in diverse conditions; and how to reduce the size of the detection
model for faster inference time. We propose to utilize upsampling and
downsampling the input eye images to the same resolution as one potential
solution for the first problem, then find out which interpolation method can
result in the highest performance of the detection model. For the second
problem, although a recent spatiotemporal convolutional neural network used for
eye blinking detection has a strong capacity to extract both spatial and
temporal characteristics, it remains having a high number of network
parameters, leading to high inference time. Therefore, using Depth-wise
Separable Convolution rather than conventional convolution layers inside each
branch is considered in this paper as a feasible solution.
