2015 International Conference on Affective Computing and Intelligent Interaction (ACII)最新文献_第2页

Psychophysiological responses to virtual crowds: Implications for wearable computing 对虚拟人群的心理生理反应:对可穿戴计算的影响

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344548

Chris G. Christou, Kyriakos Herakleous, A. Tzanavari, Charalambos (Charis) Poullis

引用次数: 9

Identifying music-induced emotions from EEG for use in brain-computer music interfacing 从脑电图中识别音乐诱发的情绪，用于脑机音乐接口

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344685

I. Daly, Asad Malik, James Weaver, F. Hwang, S. Nasuto, Duncan A. H. Williams, Alexis Kirke, E. Miranda

引用次数: 24

Deep learning vs. kernel methods: Performance for emotion prediction in videos 深度学习与核方法:视频中情绪预测的性能

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344554

Yoann Baveye, E. Dellandréa, Christel Chamaret, Liming Luke Chen

{"title":"Deep learning vs. kernel methods: Performance for emotion prediction in videos","authors":"Yoann Baveye, E. Dellandréa, Christel Chamaret, Liming Luke Chen","doi":"10.1109/ACII.2015.7344554","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344554","url":null,"abstract":"Recently, mainly due to the advances of deep learning, the performances in scene and object recognition have been progressing intensively. On the other hand, more subjective recognition tasks, such as emotion prediction, stagnate at moderate levels. In such context, is it possible to make affective computational models benefit from the breakthroughs in deep learning? This paper proposes to introduce the strength of deep learning in the context of emotion prediction in videos. The two main contributions are as follow: (i) a new dataset, composed of 30 movies under Creative Commons licenses, continuously annotated along the induced valence and arousal axes (publicly available) is introduced, for which (ii) the performance of the Convolutional Neural Networks (CNN) through supervised fine-tuning, the Support Vector Machines for Regression (SVR) and the combination of both (Transfer Learning) are computed and discussed. To the best of our knowledge, it is the first approach in the literature using CNNs to predict dimensional affective scores from videos. The experimental results show that the limited size of the dataset prevents the learning or finetuning of CNN-based frameworks but that transfer learning is a promising solution to improve the performance of affective movie content analysis frameworks as long as very large datasets annotated along affective dimensions are not available.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"61 1","pages":"77-83"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91218702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 68

Automatic discrimination of laughter using distributed sEMG 基于分布式表面肌电信号的笑声自动识别

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344644

S. Cosentino, S. Sessa, W. Kong, Di Zhang, A. Takanishi, N. Bianchi-Berthouze

{"title":"Automatic discrimination of laughter using distributed sEMG","authors":"S. Cosentino, S. Sessa, W. Kong, Di Zhang, A. Takanishi, N. Bianchi-Berthouze","doi":"10.1109/ACII.2015.7344644","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344644","url":null,"abstract":"Laughter is a very interesting non-verbal human vocalization. It is classified as a semi voluntary behavior despite being a direct form of social interaction, and can be elicited by a variety of very different stimuli, both cognitive and physical. Automatic laughter detection, analysis and classification will boost progress in affective computing, leading to the development of more natural human-machine communication interfaces. Surface Electromyography (sEMG) on abdominal muscles or invasive EMG on the larynx show potential in this direction, but these kinds of EMG-based sensing systems cannot be used in ecological settings due to their size, lack of reusability and uncomfortable setup. For this reason, they cannot be easily used for natural detection and measurement of a volatile social behavior like laughter in a variety of different situations. We propose the use of miniaturized, wireless, dry-electrode sEMG sensors on the neck for the detection and analysis of laughter. Even if with this solution the activation of specific larynx muscles cannot be precisely measured, it is possible to detect different EMG patterns related to larynx function. In addition, integrating sEMG analysis on a multisensory compact system positioned on the neck would improve the overall robustness of the whole sensing system, enabling the synchronized measure of different characteristics of laughter, like vocal production, head movement or facial expression; being at the same time less intrusive, as the neck is normally more accessible than abdominal muscles. In this paper, we report laughter discrimination rate obtained with our system depending on different conditions.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"1 1","pages":"691-697"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90460660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

The platformer experience dataset 平台游戏体验数据集

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344647

K. Karpouzis, Georgios N. Yannakakis, Noor Shaker, S. Asteriadis

引用次数: 35

Expression training for complex emotions using facial expressions and head movements 通过面部表情和头部动作进行复杂情绪的表达训练

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344660

Andra Adams, P. Robinson

引用次数: 4

Understanding speaking styles of internet speech data with LSTM and low-resource training 利用LSTM和低资源训练来理解网络语音数据的说话风格

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344667

Xixin Wu, Zhiyong Wu, Yishuang Ning, Jia Jia, Lianhong Cai, H. Meng

{"title":"Understanding speaking styles of internet speech data with LSTM and low-resource training","authors":"Xixin Wu, Zhiyong Wu, Yishuang Ning, Jia Jia, Lianhong Cai, H. Meng","doi":"10.1109/ACII.2015.7344667","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344667","url":null,"abstract":"Speech are widely used to express one's emotion, intention, desire, etc. in social network communication, deriving abundant of internet speech data with different speaking styles. Such data provides a good resource for social multimedia research. However, regarding different styles are mixed together in the internet speech data, how to classify such data remains a challenging problem. In previous work, utterance-level statistics of acoustic features are utilized as features in classifying speaking styles, ignoring the local context information. Long short-term memory (LSTM) recurrent neural network (RNN) has achieved exciting success in lots of research areas, such as speech recognition. It is able to retrieve context information for long time duration, which is important in characterizing speaking styles. To train LSTM, huge number of labeled training data is required. While for the scenario of internet speech data classification, it is quite difficult to get such large scale labeled data. On the other hand, we can get some publicly available data for other tasks (such as speech emotion recognition), which offers us a new possibility to exploit LSTM in the low-resource task. We adopt retraining strategy to train LSTM to recognize speaking styles in speech data by training the network on emotion and speaking style datasets sequentially without reset the weights of the network. Experimental results demonstrate that retraining improves the training speed and the accuracy of network in speaking style classification.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"24 1","pages":"815-820"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77004724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a minimal representation of affective gestures (Extended abstract) 情感手势的最小表示(扩展抽象)

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344616

D. Glowinski, M. Mortillaro, K. Scherer, N. Dael, G. Volpe, A. Camurri

{"title":"Towards a minimal representation of affective gestures (Extended abstract)","authors":"D. Glowinski, M. Mortillaro, K. Scherer, N. Dael, G. Volpe, A. Camurri","doi":"10.1109/ACII.2015.7344616","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344616","url":null,"abstract":"How efficiently decoding affective information when computational resources and sensor systems are limited? This paper presents a framework for analysis of affective behavior starting with a reduced amount of visual information related to human upper-body movements. The main goal is to individuate a minimal representation of emotional displays based on non-verbal gesture features. The GEMEP (Geneva multimodal emotion portrayals) corpus was used to validate this framework. Twelve emotions expressed by ten actors form the selected data set of emotion portrayals. Visual tracking of trajectories of head and hands was performed from a frontal and a lateral view. Postural/shape and dynamic expressive gesture features were identified and analyzed. A feature reduction procedure was carried out, resulting in a four-dimensional model of emotion expression, that effectively classified/grouped emotions according to their valence (positive, negative) and arousal (high, low). These results show that emotionally relevant information can be detected/measured/obtained from the dynamic qualities of gesture. The framework was implemented as software modules (plug-ins) extending the EyesWeb XMI Expressive Gesture Processing Library and was tested as a component for a multimodal search engine in collaboration with Google within the EU-ICT I-SEARCH project.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"120 1","pages":"498-504"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77894822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Recognizing emotions in dialogues with acoustic and lexical features 利用语音和词汇特征识别对话中的情绪

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344651

Leimin Tian, Johanna D. Moore, Catherine Lai

引用次数: 11

Design of intelligent emotion feedback to assist users regulate emotions: Framework and principles 帮助用户调节情绪的智能情绪反馈设计:框架与原则

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344687

Yu Hao, Donghai Wang, J. Budd

{"title":"Design of intelligent emotion feedback to assist users regulate emotions: Framework and principles","authors":"Yu Hao, Donghai Wang, J. Budd","doi":"10.1109/ACII.2015.7344687","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344687","url":null,"abstract":"Positive environmental emotion feedback is important to influence the brain and behaviors. By measuring emotional signals and providing affective neurofeedback, people can be better aware of their emotional state in real time. However, such direct mapping does not necessarily motivate people's emotion regulation effort. We introduce two levels of emotion feedback: an augmentation level that indicates direct feedback mapping and an intervention level which means feedback output is dynamically adapted with the regulation process. For the purpose of emotion regulation, this research summarizes the framework of emotion feedback design by adding new components that involve feature wrapping, mapping to output representation and interactive interface representation. By this means, the concept of intelligent emotion feedback is illustrated that not only enhances emotion regulation motivation but also considers subject and trial variability based on individual calibration and learning. An affective Brain-computer Interface technique is used to design the prototype among alternatives. Experimental tests and model simulation are planned for further evaluation.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"1 1","pages":"938-943"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72726174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6