激光与光电子学进展, 2020, 57 (18): 181026, 网络出版: 2020-09-02
基于眼部数据的挡车工表情识别方法 下载: 804次
Facial Expression Recognition Method of Spinner Based on Eye Data
摘要
为解决挡车工表情因受光照不足、遮挡等问题而导致识别精度过低的问题,构建了一种基于迁移学习的卷积神经网络模型。该模型通过对纱线质量指标的分析,确定了挡车工表情的分类标准,建立了挡车工表情数据集,同时对数据集进行直方图均衡化、Rudin-Osher-Fatemi(ROF)去噪、人脸校正等预处理。在截取挡车工实时眼部数据的基础上,利用迁移学习方法对挡车工表情识别模型进行训练。最后,通过实验验证,结果表明构建的挡车工表情识别模型的识别精度高达98%,有效地解决了因受光照、遮挡等问题而导致挡车工表情无法识别的问题。
Abstract
In order to solve the problem of low recognition accuracy caused by insufficient illumination and occlusion, a convolutional neural network model based on transfer learning was constructed. Based on the analysis of yarn quality index, the classification standard of the expression of the spinner was determined, and the expression data set was established. At the same time, the data set was preprocessed by histogram equalization, ROF (Rudin-Osher-Fatemi) denoising and facial correction. On the basis of intercepting the real-time eye data of the spinner, the transfer learning method was used to train the expression recognition model. Finally, through the experimental verification, it is shown that the recognition accuracy of the proposed model was as high as 98%, which effectively solves the problem that the spinner expression can not be recognized due to illumination and occlusion.
邵景峰, 冯海强. 基于眼部数据的挡车工表情识别方法[J]. 激光与光电子学进展, 2020, 57(18): 181026. Jingfeng Shao, Haiqiang Feng. Facial Expression Recognition Method of Spinner Based on Eye Data[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181026.