基于兴趣区域深度神经网络的静态面部表情识别

通过在面部表情数据集上训练深度卷积神经网络、深度稀疏校正神经网络两种模型,对两种深度神经网络在静态面部表情识别方面的应用作了对比和分析.基于面部表情的结构先验知识,提出一种面向面部表情识别的改良方法——K兴趣区域方法,该方法在构建的开放实验数据集上,降低了由于训练数据过少而导致深度神经网络模型泛化能力不佳的问题,使得混合模型普遍且显著地降低了测试错误率.进而,结合实验结果进行了深入分析,并对深度神经网络在任意图像数据集上的可能有效性进行了深入剖析和分析.

Static Facial Expression Recognition System Using ROI Deep Neural Networks

By building two models including Deep Convolutional Neural Networks and Deep Sparse Rectifier Neural Networks on facial expression dataset,we made contrastive evaluations in facial expression recognition system with deep neural networks.Based on prior structure knowledge of facial expression,we proposed a fast and simple improved method called K Region Of Interest——''K-ROI'',which relieved the poor generalization of deep neural networks on experimental dataset due to insufficient data and decreased the testing error rate apparently and generally.Finally,we infer the experimental results and analyze comprehensively for the possible validity with deep neural networks on arbitrary image dataset.