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.

本文旨在解决在野外环境下眼部闪烁检测面临的问题，主要通过上采样和下采样的方法解决图像分辨率和检测效率问题。同时采用深度可分卷积方法能够在提取空间和时间特征的同时减小网络规模。

野外眨眼检测的深度可分离卷积时空金字塔卷积神经网络

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

Very deep convolutional neural networks (CNNs) have been firmly established
as the primary methods for many computer vision tasks. However, most
state-of-the-art CNNs are large, which results in high inference latency.
Recently, depth-wise separable convolution has been proposed for image
recognition tasks on computationally limited platforms such as robotics and
self-driving cars. Though it is much faster than its counterpart, regular
convolution, accuracy is sacrificed. In this paper, we propose a novel
decomposition approach based on SVD, namely depth-wise decomposition, for
expanding regular convolutions into depthwise separable convolutions while
maintaining high accuracy. We show our approach can be further generalized to
the multi-channel and multi-layer cases, based on Generalized Singular Value
Decomposition (GSVD) [59]. We conduct thorough experiments with the latest
ShuffleNet V2 model [47] on both random synthesized dataset and a large-scale
image recognition dataset: ImageNet [10]. Our approach outperforms channel
decomposition [73] on all datasets. More importantly, our approach improves the
Top-1 accuracy of ShuffleNet V2 by ~2%.

本文提出了一种基于 SVD 的分解方法，将常规卷积扩展为深度可分离卷积，从而在维持高准确性的同时，优于通道分解方法，提高了 ShuffleNet V2 模型的 Top-1 准确率约 2％。