Detection of eye state can assist the related work in the field of computer vision such as face recognition, expression recognition, pose estimation and human–computer interaction. This paper proposes an Weight Binarization Convolution Neural Network and Transfer Learning (WBCNNTL) based eye state detection method, in which the WBCNNTL is composed of deep convolution neural network and the weight is binarized. The human eye state features can be extracted by Convolutional Neural Network (CNN) effectively, and binary network not only speeds up the computation, but also helps to reduce the storage space and fewer parameters of the model. Transfer learning applies the knowledge or patterns learned from the source domain to different but related fields or problems, which improves the training efficiency of the new model. Experiments on eye state detection are performed using Closed Eyes in the wild (CEW), FER2013 and Zhejiang University Eyeblink (ZJU) Databases, from which the experiment results show the average accuracy obtained by our method are 97.41% on CEW and are 97.15% on ZJU, the computing speed of binary network is faster than non-binary network. Moreover, our method requires less storage space due to lightweight binary model, which maintains better detection capability on CEW compared with some state-of-the-art works.

眼睛状态检测可以辅助人脸识别、表情识别、姿态估计和人机交互等计算机视觉领域的相关工作。本文提出了一种基于权重二值化卷积神经网络和转移学习（WBCNNTL）的眼睛状态检测方法，其中WBCNNTL由深度卷积神经网络组成，权重被二值化。卷积神经网络（CNN）可以有效地提取人眼状态特征，二进制网络不仅加快了计算速度，而且有助于减少模型的存储空间和更少的参数。迁移学习将从源领域学到的知识或模式应用于不同但相关的领域或问题，从而提高新模型的训练效率。使用Closed Eyes in the wild（CEW）、FER2013和浙江大学Eyeblink（ZJU）数据库进行眼睛状态检测实验，实验结果表明，我们的方法在CEW上获得的平均准确率为97.41%，在CEW上为97.15%。 ZJU，二进制网络的计算速度比非二进制网络要快。此外，由于轻量级二进制模型，我们的方法需要更少的存储空间，与一些最先进的作品相比，它在 CEW 上保持了更好的检测能力。