基于分布式边缘计算的情绪识别系统

doi:10.11896/jsjkx.201000010

摘要/Abstract

摘要： 近年来,边缘计算和人工智能结合的模式越来越流行。面部动作单元(ActionUnit)检测分析是一种通过分析局部面部区域中某些原子肌肉运动的线索来识别面部表情的方法。根据面部特征点的检测,可以计算出AU的值,然后通过对这些AU值进行分类来进行实时情绪检测。然而,在实际的生产过程中,由于传输面部动作单元特征数据网络的开销巨大,这会给在生产中的通信网络带来新的挑战,因此可以选择使用树莓派,实验中设计了基于轻量级边缘计算的分布式系统,优化了数据传输和组件部署。将部分计算任务转移到服务器附近,前端和后端处理模式分开可以有效缩短往返延迟,从而完成复杂的计算任务,并提高可靠性,大规模连接服务。

关键词: 边缘计算, 分布式计算, 面部动作单元, 情绪识别, 树莓派

Abstract: In recent years,the combination of edge computing and artificial intelligence has become more and more popular.Facial action unit (AU) detection recognizes facial expressions by analyzing cues about the movement of certain atomic muscles in the local facial area.According to the detection of facial feature points,we can calculate the values of AU,and then use classification algorithms for emotion recognition.However,in the actual production process,due to the tremendous network overhead of transferring the facial action unit feature data,it poses new challenges of this system being deployed in a distributed manner while running in production.Therefore,we design a lightweight edge computing based distributed system using Raspberry Pi tailed for this need,and optimize the data transfer and components deployment.In the vicinity,the front-end and back-end processing modes are separated to reduce round-trip delay,thereby completing complex computing tasks and providing high-reliability,large-scale connection services.

Key words: Distributed computing, Eedge computing, Emotion recognition, Facial action unit, Raspberry Pi

中图分类号:

TP183

钱甜甜, 张帆. 基于分布式边缘计算的情绪识别系统[J]. 计算机科学, 2021, 48(6A): 638-643. https://doi.org/10.11896/jsjkx.201000010

QIAN Tian-tian, ZHANG Fan. Emotion Recognition System Based on Distributed Edge Computing[J]. Computer Science, 2021, 48(6A): 638-643. https://doi.org/10.11896/jsjkx.201000010

参考文献

[1] BALTRUSAITI T,MAHMOUD M,ROBINSON P.Cross-dataset learning and person-specific normalisation for automatic action unit detection[C]//2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).IEEE,2015,6:1-6.
[2] ZADEH A,BALTRUSAITIS T,MORENCY L P.Convolutional experts network for facial landmark detection[C]//Proceedings of the International Conference on Computer Vision & Pattern Recognition (CVPRW),Faces-in-the-wild Work-shop/Challenge,2017,3(5):11.
[3] MERKEL D.Docker:lightweight linux containers for consistent development and deployment[J].Linux Journal,2014,2014(239):2.
[4] LUCEY P,COHN J F,KANADE T,et al.The Extended Cohn-Kanade Dataset (CK+)_A complete dataset for action unit and emotion-specified expression[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).2010:94-101.
[5] PETER R,RANA E K.Computation of emotions in man and machines[J].Philosophical Transactions of the Royal Society of London,2009,364(1535):3441-3447.
[6] MATSUYAMA Y,BHARDWAJ A,ZHAO R,et al.Socially-aware animated intelligent personal assistant agent[C]//Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue.2016:224-227.
[7] MCDUFF D,EL KALIOUBY R,DEMIRDJIAN D,et al.Predicting online media effectiveness based on smile responses gathered over the internet[C]//FG.2013.
[8] GIRARD M,COHN J F,MAHOOR M H,et al.Social risk and depression:Evidence from manual and automatic facial expression analysis[C]//IEEE International Conference & Workshops on Automatic Face & Gesture Recognition.2013.
[9] EKMAN R.What the face reveals:Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS)[M].Oxford University Press,USA,1997.
[10] HAGER J C.A comparison of units for visually measuring facial actions[J].Behavior Research Methods Instruments & Computers,1985,17(4):450-468.
[11] WANG T,ZHANG G,LIU A,et al.A Secure IoT Service Architecture with an Efficient Balance Dynamics Based on Cloud and Edge Computing[J].IEEE Internet of Things Journal,2019,6(3):4831-4843.
[12] ZHOU Z,CHEN X,LI E,et al.Edge intelligence:Paving the last mile of artificial intelligence with edge computing[J].Proceedings of the IEEE,2019,107(8):1738-1762.
[13] ZHANG X,WANG Y,LU S,et al.Openei:An open framework for edge intelligence[J].CoRR,abs/1906.01864,2019.
[14] YI S,LI C,LI Q.A survey of fog computing:concepts,applications and issues[C]//Proceedings of the 2015 Workshop on Mobile Big Data.ACM,2015:37-42.
[15] ZHANG H,LIU B,SUSANTO H,et al.Incentive mechanismfor proximity-based Mobile Crowd Service systems[C]//IEEE INFOCOM 2016-the 35th IEEE International Conference on Computer Communications.2016:1-5.
[16] OTTENWALDER B,KOLDEHOFE B,ROTHERMEL K,et al.Migcep:Operator Migration for Mobility Driven Distributed Complex Event Processing[C]//Proc.of ACM DEBS.2013:183-194.
[17] ABE Y,GEAMBASU R,JOSHI K,et al.vTube:efficientstreaming of virtual appliances over lastmile networks[C]//Proc.of the 4th annual Symposium on Cloud Computing.ACM,2013.
[18] HA K,CHEN Z,HU W,et al.Towards wearable cognitive assistance[C]//Proc.of ACM MobiSys.2014.
[19] WANG S,URGAONKAR R,ZAFER M,et al.Dynamic service migration in mobile edge-clouds[C]//2015 IFIP Networking Conference (IFIP Networking).IEEE,2015:1-9.
[20] WANG X,HAN Y,WANG C,et al.In-Edge AI:Intelligentizing Mobile Edge Computing,Caching and Communication by Federated Learning[J].IEEE Network,2019,33(5):156-165.
[21] WANG T,LUO H,ZHENG X,et al.Crowd sourcing mecha-nism for trust evaluation in cpcs based on intelligent mobile edge computing[J].ACM Transactions on Intelligent Systems and Technology (TIST),2019,10(6):62.
[22] MICHAEL J,LYONS S,AKEMASTU M,et al.GYOBA.Coding Facial Expressions with Gabor Wavelets[C]//3rd IEEE International Conference on Automatic Face and Gesture Recognition.1998:200-205.
[23] SAGONAS C,TZIMIROPOULOS G,ZAFEIRIOU S,et al.300facesin-the-wild challenge:The first facial landmark localization challenge[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2013:397-403.
[24] SAGONAS C,TZIMIROPOULOS G,ZAFEIRIOU S,et al.Asemi-automatic methodology for facial landmark annotation[C]//Proceedings of the IEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR-W).Workshop on Analysis and Modeling of Faces and Gestures.2013.
[25] ZHU X,RAMANAN D.Face detection,pose estimation,andland-mark localization in the wild[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2012:2879-2886.
[26] SAGONAS C,TZIMIROPOULOS G,ZAFEIRIOU S,et al.300facesinthewild challenge:The first facial landmark localization challenge[C]//ICCV.2013.
[27] BELHUMEUR P N,JACOBS D W,KRIEGMAN D J,et al.Localizing parts of faces using a consensus of exemplars[C]//VPR.2011.
[28] LE V,BRANDT J,LIN Z,et al.Interactive facial feature localization[C]//Computer Vision-ECCV 2012.Springer,2012:679-692.
[29] ZAFEIRIOU S.The menpo facial landmark localisation chal-lenge[C]//Computer Vision and Pattern Recognition Workshops (CVPRW).2017.
[30] SCOCA V,ARAL A,BRANDIC I,et al.Scheduling Latency-Sensitive Applications in Edge Computing[C]//Closer.2018:158-168.

相关文章 15

[1]	孙慧婷, 范艳芳, 马孟晓, 陈若愚, 蔡英. VEC中基于动态定价的车辆协同计算卸载方案 Dynamic Pricing-based Vehicle Collaborative Computation Offloading Scheme in VEC 计算机科学, 2022, 49(9): 242-248. https://doi.org/10.11896/jsjkx.210700166
[2]	于滨, 李学华, 潘春雨, 李娜. 基于深度强化学习的边云协同资源分配算法 Edge-Cloud Collaborative Resource Allocation Algorithm Based on Deep Reinforcement Learning 计算机科学, 2022, 49(7): 248-253. https://doi.org/10.11896/jsjkx.210400219
[3]	李梦菲, 毛莺池, 屠子健, 王瑄, 徐淑芳. 基于深度确定性策略梯度的服务器可靠性任务卸载策略 Server-reliability Task Offloading Strategy Based on Deep Deterministic Policy Gradient 计算机科学, 2022, 49(7): 271-279. https://doi.org/10.11896/jsjkx.210600040
[4]	徐鸣珂, 张帆. Head Fusion:一种提高语音情绪识别的准确性和鲁棒性的方法 Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition 计算机科学, 2022, 49(7): 132-141. https://doi.org/10.11896/jsjkx.210100085
[5]	袁昊男, 王瑞锦, 郑博文, 吴邦彦. 基于Fabric的电子病历跨链可信共享系统设计与实现 Design and Implementation of Cross-chain Trusted EMR Sharing System Based on Fabric 计算机科学, 2022, 49(6A): 490-495. https://doi.org/10.11896/jsjkx.210500063
[6]	方韬, 杨旸, 陈佳馨. D2D辅助移动边缘计算下的卸载策略优化 Optimization of Offloading Decisions in D2D-assisted MEC Networks 计算机科学, 2022, 49(6A): 601-605. https://doi.org/10.11896/jsjkx.210200114
[7]	刘漳辉, 郑鸿强, 张建山, 陈哲毅. 多无人机使能移动边缘计算系统中的计算卸载与部署优化 Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems 计算机科学, 2022, 49(6A): 619-627. https://doi.org/10.11896/jsjkx.210600165
[8]	谢万城, 李斌, 代玥玥. 空中智能反射面辅助边缘计算中基于PPO的任务卸载方案 PPO Based Task Offloading Scheme in Aerial Reconfigurable Intelligent Surface-assisted Edge Computing 计算机科学, 2022, 49(6): 3-11. https://doi.org/10.11896/jsjkx.220100249
[9]	周天清, 岳亚莉. 超密集物联网络中多任务多步计算卸载算法研究 Multi-Task and Multi-Step Computation Offloading in Ultra-dense IoT Networks 计算机科学, 2022, 49(6): 12-18. https://doi.org/10.11896/jsjkx.211200147
[10]	彭冬阳, 王睿, 胡谷雨, 祖家琛, 王田丰. 视频缓存策略中QoE和能量效率的公平联合优化 Fair Joint Optimization of QoE and Energy Efficiency in Caching Strategy for Videos 计算机科学, 2022, 49(4): 312-320. https://doi.org/10.11896/jsjkx.210800027
[11]	高越, 傅湘玲, 欧阳天雄, 陈松龄, 闫晨巍. 基于时空自适应图卷积神经网络的脑电信号情绪识别 EEG Emotion Recognition Based on Spatiotemporal Self-Adaptive Graph ConvolutionalNeural Network 计算机科学, 2022, 49(4): 30-36. https://doi.org/10.11896/jsjkx.210900200
[12]	张海波, 张益峰, 刘开健. 基于NOMA-MEC的车联网任务卸载、迁移与缓存策略 Task Offloading,Migration and Caching Strategy in Internet of Vehicles Based on NOMA-MEC 计算机科学, 2022, 49(2): 304-311. https://doi.org/10.11896/jsjkx.210100157
[13]	林潮伟, 林兵, 陈星. 边缘环境下基于模糊理论的科学工作流调度研究 Study on Scientific Workflow Scheduling Based on Fuzzy Theory Under Edge Environment 计算机科学, 2022, 49(2): 312-320. https://doi.org/10.11896/jsjkx.201000102
[14]	王如斌, 李瑞远, 何华均, 刘通, 李天瑞. 面向海量空间数据的分布式距离连接算法 Distributed Distance Join Algorithm for Massive Spatial Data 计算机科学, 2022, 49(1): 95-100. https://doi.org/10.11896/jsjkx.210100060
[15]	梁俊斌, 张海涵, 蒋婵, 王天舒. 移动边缘计算中基于深度强化学习的任务卸载研究进展 Research Progress of Task Offloading Based on Deep Reinforcement Learning in Mobile Edge Computing 计算机科学, 2021, 48(7): 316-323. https://doi.org/10.11896/jsjkx.200800095

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed