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
Spectral energy distribution of surface EMG signal is often used but difficultly and effectively control artificial limb, because the spectral energy distribution changes in the process of limb actions. In this paper,...Spectral energy distribution of surface EMG signal is often used but difficultly and effectively control artificial limb, because the spectral energy distribution changes in the process of limb actions. In this paper, the general characteristics of surface EMG signal patterns were firstly characterized by spectral energy change. 13 healthy subjects were instructed to execute forearm supination （FS） and forearm pronation （FP） with their right foreanns when their forearm muscles were ＂fatigue＂ or ＂relaxed＂. All surface EMG signals were recorded from their right forearm flexor during their right forearm actions. Two sets of surface EMG signals were segmented from every surface EMG signal appropriately at preparing stage and acting stage. Relative wavelet packet energy （symbolized by pnp and pna respectively at preparing stage and acting stage, n denotes the nth frequency band） of surface EMG signal firstly was calculated and then, the difference （Pn = Pna-Pnp） were gained. The results showed that Pn from some frequency bands can effectively characterize the general characteristics of surface EMG signal patterns. Compared with Pn in other frequency bands, P4, the spectral energy change from 93.75 to 125 Hz, was more appropriately regarded as the features.展开更多
Human action recognition has become one of the most active research topics in human-computer interaction and artificial intelligence, and has attracted much attention. Here, we employ a low-cost optical sensor Kinect ...Human action recognition has become one of the most active research topics in human-computer interaction and artificial intelligence, and has attracted much attention. Here, we employ a low-cost optical sensor Kinect to capture the action information of the human skeleton. We then propose a two-level hierarchical human action recognition model with self-selection classifiers via skeleton data. Especially different optimal classifiers are selected by probability voting mechanism and 10 times 10-fold cross validation at different coarse grained levels. Extensive simulations on a well-known open dataset and results demonstrate that our proposed method is efficient in human action recognition, achieving 94.19%the average recognition rate and 95.61% the best rate.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China 973 Project,Grant number:2005CB724303Yunnan Education Department Project,Grant number:03Y3081
文摘Spectral energy distribution of surface EMG signal is often used but difficultly and effectively control artificial limb, because the spectral energy distribution changes in the process of limb actions. In this paper, the general characteristics of surface EMG signal patterns were firstly characterized by spectral energy change. 13 healthy subjects were instructed to execute forearm supination （FS） and forearm pronation （FP） with their right foreanns when their forearm muscles were ＂fatigue＂ or ＂relaxed＂. All surface EMG signals were recorded from their right forearm flexor during their right forearm actions. Two sets of surface EMG signals were segmented from every surface EMG signal appropriately at preparing stage and acting stage. Relative wavelet packet energy （symbolized by pnp and pna respectively at preparing stage and acting stage, n denotes the nth frequency band） of surface EMG signal firstly was calculated and then, the difference （Pn = Pna-Pnp） were gained. The results showed that Pn from some frequency bands can effectively characterize the general characteristics of surface EMG signal patterns. Compared with Pn in other frequency bands, P4, the spectral energy change from 93.75 to 125 Hz, was more appropriately regarded as the features.
基金Supported by the National Nature Science Foundation of China under Grant Nos.11475003,61603003,and 11471093the Key Project of Cultivation of Leading Talents in Universities of Anhui Province under Grant No.gxfxZD2016174+2 种基金Funds of Integration of Cloud Computing and Big DataInnovation of Science and Technology of Ministry of Education of China under Grant No.2017A09116Anhui Provincial Department of Education Outstanding Top-Notch Talent-Funded Project under Grant No.gxbjZD26
文摘Human action recognition has become one of the most active research topics in human-computer interaction and artificial intelligence, and has attracted much attention. Here, we employ a low-cost optical sensor Kinect to capture the action information of the human skeleton. We then propose a two-level hierarchical human action recognition model with self-selection classifiers via skeleton data. Especially different optimal classifiers are selected by probability voting mechanism and 10 times 10-fold cross validation at different coarse grained levels. Extensive simulations on a well-known open dataset and results demonstrate that our proposed method is efficient in human action recognition, achieving 94.19%the average recognition rate and 95.61% the best rate.
