...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Multimedia Tools and Applications >Static facial expression recognition using convolutional neural networks based on transfer learning and hyperparameter optimization
【24h】

Static facial expression recognition using convolutional neural networks based on transfer learning and hyperparameter optimization

机译：基于转移学习的卷积神经网络静态面部表情识别和封锁

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Expression recognition (ER), which has been frequently used in human-computer interaction, uses visual data such as video and static images or sensor-based data for recognizing. Facial expression recognition (FER) is a visual data based ER. Since videos have sequential images, it can be easier to recognize emotion in video signals rather than static images which consist of a single plain image. Therefore, FER on static images is a relatively tough task. Recently, deep learning methods have introduced increased success in classification problems. Accordingly, these methods are also used for FER in the literature. Data preparation and hyperparameter optimization can be utilized to increase the success of deep learning methods. With the preparation of data, the features become more pronounced. Increasing the number of training samples directly also generally affects the success rate. Tuning the hyper-parameters of deep learning is another factor that increases the performance of the models. In this study, a classification method including data preparation, hyperparameter optimization, and a transfer learning aided convolutional neural network is proposed. Through the study, a new dataset, named ERUFER, was created by using static images. The newly introduced dataset ERUFER and a popular public dataset JAFFE were classified by the proposed method. To the extent of our knowledge, the best result in the literature is achieved by the proposed method for the JAFFE dataset using a 10-fold cross-validation test technique. On the other hand, a success rate with 92.56 % is achieved for the ERUFER dataset.
机译：已经经常用于人机交互的表达式识别（ER）使用视觉数据，例如视频和静态图像或基于传感器的数据来识别。面部表情识别（FER）是基于视觉数据的ER。由于视频具有顺序图像，因此可以更容易地识别视频信号中的情绪而不是由单个普通图像组成的静态图像。因此，静态图像上的FER是一个相对艰巨的任务。最近，深入学习方法引入了增加的分类问题的成功。因此，这些方法也用于文献中的FER。数据准备和近双数计优化可用于增加深度学习方法的成功。随着数据的准备，功能变得更加明显。增加培训样本的数量直接也会影响成功率。调整深度学习的超参数是提高模型性能的另一个因素。在本研究中，提出了一种包括数据准备，封立参数优化和转移学习辅助卷积神经网络的分类方法。通过该研究，通过使用静态图像来创建一个名为Erufer的新数据集。新推出的数据集Erufer和一个流行的公共数据集jaffe被提出的方法分类。在我们的知识的范围内，通过使用10倍交叉验证测试技术的jaffe数据集的方法来实现文献的最佳结果。另一方面，对于Erufer数据集实现了92.56％的成功率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号