Yuan Zong, Wenming Zheng, Xiaohua Huang, Jingwei Yan, T. Zhang
In this paper, we propose the method using Transductive Transfer Linear Discriminant Analysis (TTLDA) and Riesz-based Volume Local Binary Patterns (RVLBP) for image based static facial expression recognition challenge of the Emotion Recognition in the Wild Challenge (EmotiW 2015). The task of this challenge is to assign facial expression labels to frames of some movies containing a face under the real word environment. In our method, we firstly employ a multi-scale image partition scheme to divide each face image into some image blocks and use RVLBP features extracted from each block to describe each facial image. Then, we adopt the TTLDA approach based on RVLBP to cope with the expression recognition task. The experiments on the testing data of SFEW 2.0 database, which is used for image based static facial expression challenge, demonstrate that our method achieves the accuracy of 50%. This result has a 10.87% improvement over the baseline provided by this challenge organizer.
{"title":"Transductive Transfer LDA with Riesz-based Volume LBP for Emotion Recognition in The Wild","authors":"Yuan Zong, Wenming Zheng, Xiaohua Huang, Jingwei Yan, T. Zhang","doi":"10.1145/2818346.2830584","DOIUrl":"https://doi.org/10.1145/2818346.2830584","url":null,"abstract":"In this paper, we propose the method using Transductive Transfer Linear Discriminant Analysis (TTLDA) and Riesz-based Volume Local Binary Patterns (RVLBP) for image based static facial expression recognition challenge of the Emotion Recognition in the Wild Challenge (EmotiW 2015). The task of this challenge is to assign facial expression labels to frames of some movies containing a face under the real word environment. In our method, we firstly employ a multi-scale image partition scheme to divide each face image into some image blocks and use RVLBP features extracted from each block to describe each facial image. Then, we adopt the TTLDA approach based on RVLBP to cope with the expression recognition task. The experiments on the testing data of SFEW 2.0 database, which is used for image based static facial expression challenge, demonstrate that our method achieves the accuracy of 50%. This result has a 10.87% improvement over the baseline provided by this challenge organizer.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82596697","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}
We conducted a feasibility study on the use of a moving auditory cue for spatial acquisition for pedestrian navigation by comparing its performance with a static auditory cue, the use of which has been investigated in previous studies. To investigate the performance of human sound azimuthal localization, we designed and conducted a controlled experiment with 15 participants and found that performance was statistically significantly more accurate with an auditory source moving from the opposite direction over users' heads to the target direction than with a static sound. Based on this finding, we designed a bimodal pedestrian navigation system using both visual and auditory feedback. We evaluated the system by conducting a field study with four users and received overall positive feedback.
{"title":"An Experiment on the Feasibility of Spatial Acquisition using a Moving Auditory Cue for Pedestrian Navigation","authors":"Yeseul Park, Kyle Koh, Heonjin Park, Jinwook Seo","doi":"10.1145/2818346.2820779","DOIUrl":"https://doi.org/10.1145/2818346.2820779","url":null,"abstract":"We conducted a feasibility study on the use of a moving auditory cue for spatial acquisition for pedestrian navigation by comparing its performance with a static auditory cue, the use of which has been investigated in previous studies. To investigate the performance of human sound azimuthal localization, we designed and conducted a controlled experiment with 15 participants and found that performance was statistically significantly more accurate with an auditory source moving from the opposite direction over users' heads to the target direction than with a static sound. Based on this finding, we designed a bimodal pedestrian navigation system using both visual and auditory feedback. We evaluated the system by conducting a field study with four users and received overall positive feedback.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84069178","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}
Immersive Virtual Reality Environments that can be accessed through multimodal natural interfaces will bring new affordances to mediated interaction with virtual embodied agents and avatars. Such interfaces will measure, amongst others, users' poses and motion which can be copied to an embodied avatar representation of the user that is situated in a virtual or augmented reality space shared with autonomous virtual agents and human controlled or semi-autonomous avatars. Designers of such environments will be challenged to facilitate believable social interactions by creating agents or semi-autonomous avatars that can respond meaningfully to users' natural behaviors, as captured by these interfaces. In our future research, we aim to realize such interactions to create rich social encounters in immersive Virtual Reality. In this current work, we present the approach we envisage to analyze and learn agent behavior from human-agent interaction in an iterative fashion. We specifically look at small-scale, `regulative' nonverbal behaviors. Agents inform their behavior on previous observations, observing responses that these behaviors elicit in new users, thus iteratively generating corpora of short, situated human-agent interaction sequences that are to be analyzed, annotated and processed to generate socially intelligent agent behavior. Some choices and challenges of this approach are discussed.
{"title":"Record, Transform & Reproduce Social Encounters in Immersive VR: An Iterative Approach","authors":"Jan Kolkmeier","doi":"10.1145/2818346.2823314","DOIUrl":"https://doi.org/10.1145/2818346.2823314","url":null,"abstract":"Immersive Virtual Reality Environments that can be accessed through multimodal natural interfaces will bring new affordances to mediated interaction with virtual embodied agents and avatars. Such interfaces will measure, amongst others, users' poses and motion which can be copied to an embodied avatar representation of the user that is situated in a virtual or augmented reality space shared with autonomous virtual agents and human controlled or semi-autonomous avatars. Designers of such environments will be challenged to facilitate believable social interactions by creating agents or semi-autonomous avatars that can respond meaningfully to users' natural behaviors, as captured by these interfaces. In our future research, we aim to realize such interactions to create rich social encounters in immersive Virtual Reality. In this current work, we present the approach we envisage to analyze and learn agent behavior from human-agent interaction in an iterative fashion. We specifically look at small-scale, `regulative' nonverbal behaviors. Agents inform their behavior on previous observations, observing responses that these behaviors elicit in new users, thus iteratively generating corpora of short, situated human-agent interaction sequences that are to be analyzed, annotated and processed to generate socially intelligent agent behavior. Some choices and challenges of this approach are discussed.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80740630","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}
We present a novel method for classifying emotions from static facial images. Our approach leverages on the recent success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike the settings often assumed there, far less labeled data is typically available for training emotion classification systems. Our method is therefore designed with the goal of simplifying the problem domain by removing confounding factors from the input images, with an emphasis on image illumination variations. This, in an effort to reduce the amount of data required to effectively train deep CNN models. To this end, we propose novel transformations of image intensities to 3D spaces, designed to be invariant to monotonic photometric transformations. These are applied to CASIA Webface images which are then used to train an ensemble of multiple architecture CNNs on multiple representations. Each model is then fine-tuned with limited emotion labeled training data to obtain final classification models. Our method was tested on the Emotion Recognition in the Wild Challenge (EmotiW 2015), Static Facial Expression Recognition sub-challenge (SFEW) and shown to provide a substantial, 15.36% improvement over baseline results (40% gain in performance).
{"title":"Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns","authors":"Gil Levi, Tal Hassner","doi":"10.1145/2818346.2830587","DOIUrl":"https://doi.org/10.1145/2818346.2830587","url":null,"abstract":"We present a novel method for classifying emotions from static facial images. Our approach leverages on the recent success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike the settings often assumed there, far less labeled data is typically available for training emotion classification systems. Our method is therefore designed with the goal of simplifying the problem domain by removing confounding factors from the input images, with an emphasis on image illumination variations. This, in an effort to reduce the amount of data required to effectively train deep CNN models. To this end, we propose novel transformations of image intensities to 3D spaces, designed to be invariant to monotonic photometric transformations. These are applied to CASIA Webface images which are then used to train an ensemble of multiple architecture CNNs on multiple representations. Each model is then fine-tuned with limited emotion labeled training data to obtain final classification models. Our method was tested on the Emotion Recognition in the Wild Challenge (EmotiW 2015), Static Facial Expression Recognition sub-challenge (SFEW) and shown to provide a substantial, 15.36% improvement over baseline results (40% gain in performance).","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78837247","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}
Viet-Cuong Ta, W. Johal, Maxime Portaz, Eric Castelli, D. Vaufreydaz
New technologies and especially robotics is going towards more natural user interfaces. Works have been done in different modality of interaction such as sight (visual computing), and audio (speech and audio recognition) but some other modalities are still less researched. The touch modality is one of the less studied in HRI but could be valuable for naturalistic interaction. However touch signals can vary in semantics. It is therefore necessary to be able to recognize touch gestures in order to make human-robot interaction even more natural. We propose a method to recognize touch gestures. This method was developed on the CoST corpus and then directly applied on the HAART dataset as a participation of the Social Touch Challenge at ICMI 2015. Our touch gesture recognition process is detailed in this article to make it reproducible by other research teams. Besides features set description, we manually filtered the training corpus to produce 2 datasets. For the challenge, we submitted 6 different systems. A Support Vector Machine and a Random Forest classifiers for the HAART dataset. For the CoST dataset, the same classifiers are tested in two conditions: using all or filtered training datasets. As reported by organizers, our systems have the best correct rate in this year's challenge (70.91% on HAART, 61.34% on CoST). Our performances are slightly better that other participants but stay under previous reported state-of-the-art results.
{"title":"The Grenoble System for the Social Touch Challenge at ICMI 2015","authors":"Viet-Cuong Ta, W. Johal, Maxime Portaz, Eric Castelli, D. Vaufreydaz","doi":"10.1145/2818346.2830598","DOIUrl":"https://doi.org/10.1145/2818346.2830598","url":null,"abstract":"New technologies and especially robotics is going towards more natural user interfaces. Works have been done in different modality of interaction such as sight (visual computing), and audio (speech and audio recognition) but some other modalities are still less researched. The touch modality is one of the less studied in HRI but could be valuable for naturalistic interaction. However touch signals can vary in semantics. It is therefore necessary to be able to recognize touch gestures in order to make human-robot interaction even more natural. We propose a method to recognize touch gestures. This method was developed on the CoST corpus and then directly applied on the HAART dataset as a participation of the Social Touch Challenge at ICMI 2015. Our touch gesture recognition process is detailed in this article to make it reproducible by other research teams. Besides features set description, we manually filtered the training corpus to produce 2 datasets. For the challenge, we submitted 6 different systems. A Support Vector Machine and a Random Forest classifiers for the HAART dataset. For the CoST dataset, the same classifiers are tested in two conditions: using all or filtered training datasets. As reported by organizers, our systems have the best correct rate in this year's challenge (70.91% on HAART, 61.34% on CoST). Our performances are slightly better that other participants but stay under previous reported state-of-the-art results.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86553547","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}
Multimodal interaction frameworks are an efficient means of utilizing many existing processing and fusion techniques in a wide variety of application areas, even by non-experts. However, the application of these frameworks to highly interactive application areas like VR, AR, MR, and computer games in a reusable, modifiable, and modular manner is not straightforward. It currently lacks some software technical solutions that (1) preserve the general decoupling principle of platforms and at the same time (2) provide the required close temporal as well as semantic coupling of involved software modules and multimodal processing steps. This thesis approches current challenges and aims at providing the research community with a framework that fosters repeatability of scientific achievements and the ability to built on previous results.
{"title":"Software Techniques for Multimodal Input Processing in Realtime Interactive Systems","authors":"Martin Fischbach","doi":"10.1145/2818346.2823308","DOIUrl":"https://doi.org/10.1145/2818346.2823308","url":null,"abstract":"Multimodal interaction frameworks are an efficient means of utilizing many existing processing and fusion techniques in a wide variety of application areas, even by non-experts. However, the application of these frameworks to highly interactive application areas like VR, AR, MR, and computer games in a reusable, modifiable, and modular manner is not straightforward. It currently lacks some software technical solutions that (1) preserve the general decoupling principle of platforms and at the same time (2) provide the required close temporal as well as semantic coupling of involved software modules and multimodal processing steps. This thesis approches current challenges and aims at providing the research community with a framework that fosters repeatability of scientific achievements and the ability to built on previous results.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88977095","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}
Understanding audience engagement levels for presentations has the potential to enable richer and more focused interaction with audio-visual recordings. We describe an investigation into automated analysis of multimodal recordings of scientific talks where the use of modalities most typically associated with engagement such as eye-gaze is not feasible. We first study visual and acoustic features to identify those most commonly associated with good speaking techniques. To understand audience interpretation of good speaking techniques, we angaged human annotators to rate the qualities of the speaker for a series of 30-second video segments taken from a corpus of 9 hours of presentations from an academic conference. Our annotators also watched corresponding video recordings of the audience to presentations to estimate the level of audience engagement for each talk. We then explored the effectiveness of multimodal features extracted from the presentation video against Likert-scale ratings of each speaker as assigned by the annotators. and on manually labelled audience engagement levels. These features were used to build a classifier to rate the qualities of a new speaker. This was able classify a rating for a presenter over an 8-class range with an accuracy of 52%. By combining these classes to a 4-class range accuracy increases to 73%. We analyse linear correlations with individual speaker-based modalities and actual audience engagement levels to understand the corresponding effect on audience engagement. A further classifier was then built to predict the level of audience engagement to a presentation by analysing the speaker's use of acoustic and visual cues. Using these speaker based modalities pre-fused with speaker ratings only, we are able to predict actual audience engagement levels with an accuracy of 68%. By combining with basic visual features from the audience as whole, we are able to improve this to an accuracy of 70%.
{"title":"Effects of Good Speaking Techniques on Audience Engagement","authors":"Keith Curtis, G. Jones, N. Campbell","doi":"10.1145/2818346.2820766","DOIUrl":"https://doi.org/10.1145/2818346.2820766","url":null,"abstract":"Understanding audience engagement levels for presentations has the potential to enable richer and more focused interaction with audio-visual recordings. We describe an investigation into automated analysis of multimodal recordings of scientific talks where the use of modalities most typically associated with engagement such as eye-gaze is not feasible. We first study visual and acoustic features to identify those most commonly associated with good speaking techniques. To understand audience interpretation of good speaking techniques, we angaged human annotators to rate the qualities of the speaker for a series of 30-second video segments taken from a corpus of 9 hours of presentations from an academic conference. Our annotators also watched corresponding video recordings of the audience to presentations to estimate the level of audience engagement for each talk. We then explored the effectiveness of multimodal features extracted from the presentation video against Likert-scale ratings of each speaker as assigned by the annotators. and on manually labelled audience engagement levels. These features were used to build a classifier to rate the qualities of a new speaker. This was able classify a rating for a presenter over an 8-class range with an accuracy of 52%. By combining these classes to a 4-class range accuracy increases to 73%. We analyse linear correlations with individual speaker-based modalities and actual audience engagement levels to understand the corresponding effect on audience engagement. A further classifier was then built to predict the level of audience engagement to a presentation by analysing the speaker's use of acoustic and visual cues. Using these speaker based modalities pre-fused with speaker ratings only, we are able to predict actual audience engagement levels with an accuracy of 68%. By combining with basic visual features from the audience as whole, we are able to improve this to an accuracy of 70%.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79555617","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}
Emotion recognition in the wild is a very challenging task. In this paper, we propose a multiple models fusion method to automatically recognize the expression in the video clip as part of the third Emotion Recognition in the Wild Challenge (EmotiW 2015). In our method, we first extract dense SIFT, LBP-TOP and audio features from each video clip. For dense SIFT features, we use the bag of features (BoF) model with two different encoding methods (locality-constrained linear coding and group saliency based coding) to further represent it. During the classification process, we use partial least square regression to calculate the regression value of each model. By learning the optimal weight of each model based on the regression value, we fuse these models together. We conduct experiments on the given validation and test datasets, and achieve superior performance. The best recognition accuracy of our fusion method is 52.50% on the test dataset, which is 13.17% higher than the challenge baseline accuracy of 39.33%.
{"title":"Multiple Models Fusion for Emotion Recognition in the Wild","authors":"Jianlong Wu, Zhouchen Lin, H. Zha","doi":"10.1145/2818346.2830582","DOIUrl":"https://doi.org/10.1145/2818346.2830582","url":null,"abstract":"Emotion recognition in the wild is a very challenging task. In this paper, we propose a multiple models fusion method to automatically recognize the expression in the video clip as part of the third Emotion Recognition in the Wild Challenge (EmotiW 2015). In our method, we first extract dense SIFT, LBP-TOP and audio features from each video clip. For dense SIFT features, we use the bag of features (BoF) model with two different encoding methods (locality-constrained linear coding and group saliency based coding) to further represent it. During the classification process, we use partial least square regression to calculate the regression value of each model. By learning the optimal weight of each model based on the regression value, we fuse these models together. We conduct experiments on the given validation and test datasets, and achieve superior performance. The best recognition accuracy of our fusion method is 52.50% on the test dataset, which is 13.17% higher than the challenge baseline accuracy of 39.33%.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81101790","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}
Detecting a fall before it actually happens will positively affect lives of the elderly. While the main causes of falling are related to postural sway and walking, determining abnormalities in one of these activities or both of them would be informative to predicting the fall probability. A need exists for a portable gait and postural sway analysis system that can provide individuals with real-time information about changes and quality of gait in the real world, not just in a laboratory. In this research project I aim to build a multi-modal system that finds the correlation between vision extracted features and accelerometer and force plate data to determine a general gait and body sway pattern. Then this information is used to assess a difference to normative age and gender relevant patterns as well as any changes over time. This could provide a core indicator of broader health and function in ageing and disease.
{"title":"Gait and Postural Sway Analysis, A Multi-Modal System","authors":"Hafsa Ismail","doi":"10.1145/2818346.2823310","DOIUrl":"https://doi.org/10.1145/2818346.2823310","url":null,"abstract":"Detecting a fall before it actually happens will positively affect lives of the elderly. While the main causes of falling are related to postural sway and walking, determining abnormalities in one of these activities or both of them would be informative to predicting the fall probability. A need exists for a portable gait and postural sway analysis system that can provide individuals with real-time information about changes and quality of gait in the real world, not just in a laboratory. In this research project I aim to build a multi-modal system that finds the correlation between vision extracted features and accelerometer and force plate data to determine a general gait and body sway pattern. Then this information is used to assess a difference to normative age and gender relevant patterns as well as any changes over time. This could provide a core indicator of broader health and function in ageing and disease.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88636158","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}
{"title":"Session details: Poster Session","authors":"R. Horaud, D. Bohus","doi":"10.1145/3252452","DOIUrl":"https://doi.org/10.1145/3252452","url":null,"abstract":"","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85623048","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}
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