摘要
深度神经网络已经被证明在图像、语音、文本领域具有挖掘数据深层潜在的分布式表达特征的能力.通过在多个面部情感数据集上训练深度卷积神经网络和深度稀疏校正神经网络两种深度学习模型,对深度神经网络在面部情感分类领域的应用作了对比评估.进而,引入了面部结构先验知识,结合感兴趣区域(Region of interest,ROI)和K最近邻算法(K-nearest neighbors,KNN),提出一种快速、简易的针对面部表情分类的深度学习训练改进方案—ROI-KNN,该训练方案降低了由于面部表情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,提高了深度学习在面部表情分类中的鲁棒性,同时,显著地降低了测试错误率.
Deep neural networks have been proved to be able to mine distributed representation of data including image,speech and text. By building two models of deep convolutional neural networks and deep sparse rectifier neural networks on facial expression dataset, we make contrastive evaluations in facial expression recognition system with deep neural networks. Additionally, combining region of interest（ROI） and K-nearest neighbors（KNN）, we propose a fast and simple improved method called ＂ROI-KNN＂ for facial expression classification, which relieves the poor generalization of deep neural networks due to lacking of data and decreases the testing error rate apparently and generally. The proposed method also improves the robustness of deep learning in facial expression classification.
出处
《自动化学报》
EI
CSCD
北大核心
2016年第6期883-891,共9页
Acta Automatica Sinica
基金
国家自然科学基金重点项目(61432004)
安徽省自然科学基金(1508085QF119)
模式识别国家重点实验室开放课题(NLPR201407345)
中国博士后科学基金(2015M580532)
合肥工业大学2015年国家省级大学生创新训练计划项目(2015cxcys109)资助~~
关键词
卷积神经网络
面部情感识别
模型泛化
先验知识
Convolution neural networks
facial expression recognition
model generalization
prior knowledge
