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Eye blinking detection in the wild plays an essential role in deceptiondetection, driving fatigue detection, etc. Despite the fact that numerousattempts have already been made, the majority of them have encountereddifficulties, such as the derived eye images having different resolutions asthe distance between the face and the camera changes; or the requirement of alightweight detection model to obtain a short inference time in order toperform in real-time. In this research, two problems are addressed: how the eyeblinking detection model can learn efficiently from different resolutions ofeye pictures in diverse conditions; and how to reduce the size of the detectionmodel for faster inference time. We propose to utilize upsampling anddownsampling the input eye images to the same resolution as one potentialsolution for the first problem, then find out which interpolation method canresult in the highest performance of the detection model. For the secondproblem, although a recent spatiotemporal convolutional neural network used foreye blinking detection has a strong capacity to extract both spatial andtemporal characteristics, it remains having a high number of networkparameters, leading to high inference time. Therefore, using Depth-wiseSeparable Convolution rather than conventional convolution layers inside eachbranch is considered in this paper as a feasible solution.

Spatiotemporal Pyramidal CNN with Depth-Wise Separable Convolution for Eye Blinking Detection in the Wild