文章基本信息

标题：CNN-based Broad Learning with Efficient Incremental Reconstruction Model for Facial Emotion Recognition ⁎
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作者：Luefeng Chen ; Min Li ; Xuzhi Lai 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：10236-10241
DOI：10.1016/j.ifacol.2020.12.2754
语种：English
出版社：Elsevier
摘要：AbstractConvolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance throughk-fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network.
关键词：KeywordsConvolution Neural NetworksBroad LearningEmotion RecognitionHuman-Robot Interaction