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A pruning-then-quantization model compression framework for facial emotion recognition

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Intelligent and Converged Networks 2023年第3期4卷 225-236页

作者： Han Sun Wei Shao Tao Li Jiayu Zhao Weitao Xu Linqi Song the Department of Computer Science City University of Hong KongHong Kong 999077China the Department of Computer Science City University of Hong KongHong Kong 999077Chinaand also with City University of Hong Kong Shenzhen Research InstituteShenzhen 518057China the Shenzhen Konka Electronic Technology Co. Ltd.Shenzhen 518053China

facial emotion recognition achieves great success with the help of large neural models but also fails to be applied in practical situations due to the large model size of neural *** bridge this gap,in this paper,we combine two mainstream model compression methods(pruning and quantization)together,and propose a pruningthen-quantization framework to compress the neural models for facial emotion recognition *** on three datasets show that our model could achieve a high model compression ratio and maintain the model’s high performance ***,We analyze the layer-wise compression performance of our proposed framework to explore its effect and adaptability in fine-grained modules.

关键词： model compression facial emotion recognition Resnet

A Multi-scale Attention-based facial emotion recognition Method Based on Deep Learning

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Instrumentation 2022年第3期9卷 51-58页

作者： ZHANG Ning ZHANG Xiufeng FU Xingkui QI Guobin Dalian Minzu University Dalian 116600

Recently,people have been paying more and more attention to mental health,such as depression,autism,and other common mental *** order to achieve a mental disease diagnosis,intelligent methods have been actively ***,the existing models suffer the accuracy degradation caused by the clarity and occlusion of human faces in practical *** paper,thus,proposes a multi-scale feature fusion network that obtains feature information at three scales by locating the sentiment region in the image,and integrates global feature information and local feature *** addition,a focal cross-entropy loss function is designed to improve the network's focus on difficult samples during training,enhance the training effect,and increase the model recognition *** results on the challenging RAF_DB dataset show that the proposed model exhibits better facial expression recognition accuracy than existing techniques.

关键词： Mental Health facial emotion recognition Deep Learning Multiscale Loss Function

Pri-EMO:A universal perturbation method for privacy preserving facial emotion recognition

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Journal of Information and Intelligence 2023年第4期1卷 330-340页

作者： Yong Zeng Zhenyu Zhang Jiale Liu Jianfeng Ma Zhihong Liu School of Cyber Engineering Xidian UniversityXi'an 710071China

facial emotion have great significance in human-computer interaction,virtual reality and people's *** methods for facial emotion privacy mainly concentrate on the perturbation of facial emotion ***,cryptography-based perturbation algorithms are highly computationally expensive,and transformation-based perturbation algorithms only target specific recognition *** this paper,we propose a universal feature vector-based privacy-preserving perturbation algorithm for facial *** method implements privacy-preserving facial emotion images on the feature space by computing tiny perturbations and adding them to the original *** addition,the proposed algorithm can also enable expression images to be recognized as specific *** show that the protection success rate of our method is above 95%and the image quality evaluation degrades no more than *** quantitative and qualitative results show that our proposed method has a balance between privacy and usability.

关键词： facial emotion recognition Privacy preserving Perturbation Universal algorithm Feature space

A Decomposed Projective Method based on FBT-VMD and t-ELM for facial emotion recognition

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A Decomposed Projective Method based on FBT-VMD and t-ELM fo...

Empathic Responses of Behavioral-Synchronization in Human-Agent Interaction

第40届中国控制会议

作者： Rongfei Chen Zixiang Fei Wenju Zhou Xiaofei Han Huaqing Teng Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Department of Design Manufacture and Engineering Management University of Strathclyde Shanghai Instrument Research Institute

The performance of affective computing, in particular, of facial emotion recognition, is often limited by the low interpretability of the network structure, parameter redundancy, the high computational complexity with large consumption caused by CNN strategy in extracting features. The decomposed projective method, which integrates dimensional conversion,signal analysis, and dimensionality reduction for facial emotion image processing, is considered in this presentation. The main contributions of this work are the following: 1) a dimensional conversion for a facial image, which transforming the 2-D image matrix into a 1-D signal using Fan Beam Transform（FBT）;2) a hierarchical analysis method for the signal with mode decomposition（VMD） for feature selection;and 3) a method combining the t-SNE algorithm and Extreme Learning Machine（ELM） is introduced to reduce the dimension of feature vectors and predict classification. Experimental results present a satisfactory performance with 80.17% accuracy.

关键词： Affective Computing facial emotion recognition Dimensionality Reduction Machine Learning

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Computers, Materials & Continua 2022年第5期71卷 3761-3784页

作者： Sung Park Seongeon Park Mincheol Whang Savannah College of Art and Design SavannahGA31401USA Sangmyung University Seoul03016Korea

Artificial entities,such as virtual agents,have become more *** long-term presence among humans requires the virtual agent’s ability to express appropriate emotions to elicit the necessary empathy from the *** empathy involves behavioral mimicry,a synchronized co-movement between dyadic ***,the characteristics of such synchrony between humans and virtual agents remain unclear in empathic *** study evaluates the participant’s behavioral synchronization when a virtual agent exhibits an emotional expression congruent with the emotional context through facial expressions,behavioral gestures,and *** viewed an emotion-eliciting video stimulus(negative or positive)with a virtual *** participants then conversed with the virtual agent about the video,such as how the participant felt about the *** virtual agent expressed emotions congruent with the video or neutral emotion during the *** participants’facial expressions,such as the facial expressive intensity and facial muscle movement,were measured during the dialog using a *** results showed the participants’significant behavioral synchronization(i.e.,cosine similarity≥.05)in both the negative and positive emotion conditions,evident in the participant’s facial mimicry with the virtual ***,the participants’facial expressions,both movement and intensity,were significantly stronger in the emotional virtual agent than in the neutral virtual *** particular,we found that the facial muscle intensity of AU45(Blink)is an effective index to assess the participant’s synchronization that differs by the individual’s empathic capability(low,mid,high).Based on the results,we suggest an appraisal criterion to provide empirical conditions to validate empathic interaction based on the facial expression measures.

关键词： facial emotion recognition facial expression virtual agent virtual human embodied conversational agent empathy human-computer interaction

Changes in social emotion recognition following traumatic frontal lobe injury

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Neural Regeneration Research 2012年第2期7卷 101-108页

作者： Ana Teresa Martins Luis Faísca Francisco Esteves Cláudia Simo Mariline Gomes Justo Angélica Muresan Alexandra Reis Department of Psychology University of Algarve Lisbon Universitary Institute

Changes in social and emotional behaviour have been consistently observed in patients with traumatic brain injury. These changes are associated with emotion recognition deficits which represent one of the major barriers to a successful familiar and social reintegration. In the present study, 32 patients with traumatic brain injury, involving the frontal lobe, and 41 age- and education-matched healthy controls were analyzed. A Go/No-Go task was designed, where each participant had to recognize faces representing three social emotions （arrogance, guilt and jealousy）. Results suggested that ability to recognize two social emotions （arrogance and jealousy） was significantly reduced in patients with traumatic brain injury, indicating frontal lesion can reduce emotion recognition ability. In addition, the analysis of the results for hemispheric lesion location （right, left or bilateral） suggested the bilateral lesion sub-group showed a lower accuracy on all social emotions.

关键词： traumatic brain injury facial emotion recognition social emotions