Micro-Expression Recognition(MER)is a challenging task as the subtle changes occur over different action regions of a face.Changes in facial action regions are formed as Action Units(AUs),and AUs in micro-expressions ...Micro-Expression Recognition(MER)is a challenging task as the subtle changes occur over different action regions of a face.Changes in facial action regions are formed as Action Units(AUs),and AUs in micro-expressions can be seen as the actors in cooperative group activities.In this paper,we propose a novel deep neural network model for objective class-based MER,which simultaneously detects AUs and aggregates AU-level features into micro-expression-level representation through Graph Convolutional Networks(GCN).Specifically,we propose two new strategies in our AU detection module for more effective AU feature learning:the attention mechanism and the balanced detection loss function.With these two strategies,features are learned for all the AUs in a unified model,eliminating the error-prune landmark detection process and tedious separate training for each AU.Moreover,our model incorporates a tailored objective class-based AU knowledge-graph,which facilitates the GCN to aggregate the AU-level features into a micro-expression-level feature representation.Extensive experiments on two tasks in MEGC 2018 show that our approach outperforms the current state-of-the-art methods in MER.Additionally,we also report our single model-based micro-expression AU detection results.展开更多
Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most ...Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most existing micro-expression recognition technologies so far focus on modeling the single category of micro-expression images and neural network structure.Aiming at the problems of low recognition rate and weak model generalization ability in micro-expression recognition, a micro-expression recognition algorithm is proposed based on graph convolution network(GCN) and Transformer model.Firstly, action unit(AU) feature detection is extracted and facial muscle nodes in the neighborhood are divided into three subsets for recognition.Then, graph convolution layer is used to find the layout of dependencies between AU nodes of micro-expression classification.Finally, multiple attentional features of each facial action are enriched with Transformer model to include more sequence information before calculating the overall correlation of each region.The proposed method is validated in CASME II and CAS(ME)^2 datasets, and the recognition rate reached 69.85%.展开更多
基金supported by the Science and Technology Development Fund of Macao(No.0035/2023/ITP1)the National Natural Science Foundation of China(Nos.U1836220 and 61672267)+2 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX19_1616)the Qing Lan Talent Program of Jiangsu ProvinceJiangsu Province Key Research and Development Plan(Industry Foresight and Key Core Technology)-Competitive Project(No.BE2020036).
文摘Micro-Expression Recognition(MER)is a challenging task as the subtle changes occur over different action regions of a face.Changes in facial action regions are formed as Action Units(AUs),and AUs in micro-expressions can be seen as the actors in cooperative group activities.In this paper,we propose a novel deep neural network model for objective class-based MER,which simultaneously detects AUs and aggregates AU-level features into micro-expression-level representation through Graph Convolutional Networks(GCN).Specifically,we propose two new strategies in our AU detection module for more effective AU feature learning:the attention mechanism and the balanced detection loss function.With these two strategies,features are learned for all the AUs in a unified model,eliminating the error-prune landmark detection process and tedious separate training for each AU.Moreover,our model incorporates a tailored objective class-based AU knowledge-graph,which facilitates the GCN to aggregate the AU-level features into a micro-expression-level feature representation.Extensive experiments on two tasks in MEGC 2018 show that our approach outperforms the current state-of-the-art methods in MER.Additionally,we also report our single model-based micro-expression AU detection results.
基金Supported by Shaanxi Province Key Research and Development Project (2021GY-280)the National Natural Science Foundation of China (No.61834005,61772417,61802304)。
文摘Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most existing micro-expression recognition technologies so far focus on modeling the single category of micro-expression images and neural network structure.Aiming at the problems of low recognition rate and weak model generalization ability in micro-expression recognition, a micro-expression recognition algorithm is proposed based on graph convolution network(GCN) and Transformer model.Firstly, action unit(AU) feature detection is extracted and facial muscle nodes in the neighborhood are divided into three subsets for recognition.Then, graph convolution layer is used to find the layout of dependencies between AU nodes of micro-expression classification.Finally, multiple attentional features of each facial action are enriched with Transformer model to include more sequence information before calculating the overall correlation of each region.The proposed method is validated in CASME II and CAS(ME)^2 datasets, and the recognition rate reached 69.85%.
