Analysis of Expressive Gesture: The EyesWeb Expressive Gesture Processing Library

Camurri, Antonio; Mazzarino, Barbara; Volpe, Gualtiero

doi:10.1007/978-3-540-24598-8_42

Cited by 75 publications

(49 citation statements)

References 8 publications

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“…Starting from the silhouette and the blobs representing the hands of the participants, five main expressive motion cues were extracted using the EyesWeb Expressive Gesture Processing Library [45]: Quantity of Motion (QoM) and Contraction Index (CI) of the body, Velocity (VEL), Acceleration (ACC) and Fluidity (FL) of the hand's barycenter.…”

Section: Body Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

Kessous¹,

Castellano

2009

J Multimodal User Interfaces

204

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In this paper a study on multimodal automatic emotion recognition during a speech-based interaction is presented. A database was constructed consisting of people pronouncing a sentence in a scenario where they interacted with an agent using speech. Ten people pronounced a sentence corresponding to a command while making 8 different emotional expressions. Gender was equally represented, with speakers of several different native languages including French, German, Greek and Italian. Facial expression, gesture and acoustic analysis of speech were used to extract features relevant to emotion. For the automatic classification of unimodal data, bimodal data and multimodal data, a system based on a Bayesian classifier was used. After performing an automatic classification of each modality, the different modalities were combined using a multimodal approach. Fusion of the modalities at the feature level (before running the classifier) and at the results level (combining results from classifier from each modality) were compared. Fusing the multimodal data resulted in a large increase in the recognition rates in comparison to the unimodal systems: the multimodal approach increased the recognition rate by more than 10% when compared to the most successful unimodal system. Bimodal emotion recognition based on all combinations of the modalities (i.e., 'face-gesture', 'facespeech' and 'gesture-speech') was also investigated. The results show that the best pairing is 'gesture-speech'. Using all three modalities resulted in a 3.3% classification improvement over the best bimodal results.

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Section: Body Feature Extractionmentioning

confidence: 99%

“…Fluidity gives a measure of the uniformity of motion, so that fluidity is considered maximum when, in the movement between two specific points of the space, the acceleration is equal to zero. It is computed as the Directness Index [45] of the trajectory followed by the velocity of hand's barycenter in the 2D plane.…”

Section: Body Feature Extractionmentioning

confidence: 99%

Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

Kessous¹,

Castellano

2009

J Multimodal User Interfaces

204

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“…The algorithms for automatic extraction of tempo, energy, and fluentness have been implemented using the EyesWeb Expressive Gesture Library [38]:…”

Section: Expressive Features Extractionmentioning

confidence: 99%

Social retrieval of music content in multi-user performance

Mancini¹,

Volpe²,

Varni³

et al. 2015

EAI Endorsed Transactions on Creative Technologies

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An emerging trend in interactive music performance consists of the audience directly participating in the performance by means of mobile devices. This is a step forward with respect to concepts like active listening and collaborative music making: non-expert members of an audience are enabled to directly participate in a creative activity such as the performance. This requires the availability of technologies for capturing and analysing in real-time the natural behaviour of the users/performers, with particular reference to nonverbal expressive and social behaviour. This paper presents a prototype of a non-verbal expressive and social search engine and active listening system, enabling two teams of non-expert users to act as performers. The performance consists of real-time sonic manipulation and mixing of music pieces selected according to features characterising performers' movements captured by mobile devices. The system is described with specific reference to the SIEMPRE Podium Performance, a non-verbal socio-mobile music performance presented at the Art & ICT Exhibition that took place in Vilnius (LI) in November 2013.

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“…It is interesting to note that HandVu and GART can be combined to allow robust hand tracking and a larger number of gestures (combining postures and movement, like Gestures2Go) than either one isolated. Finally, EyesWeb [Camurri et al 2003] is a framework with a graphical programming interface that presents several tools and metrics for segmentation and analysis of full body movements.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of a Flexible Hand Gesture Command Interface for Games Based on Computer Vision

Bernardes

Nakamura

Tori

2009

2009 VIII Brazilian Symposium on Games and Digital Entertainment

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This paper describes a command interface for games based on hand gestures defined by postures, movement and location. The large variety of gestures thus possible increases usability by allowing a better match between gesture and action. The system uses computer vision requiring no sensors or markers on the user or background. The analysis of requirements for games, the architecture and implementation are discussed, as well as the results of several tests to evaluate how well each requirement is met.

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Analysis of Expressive Gesture: The EyesWeb Expressive Gesture Processing Library

Cited by 75 publications

References 8 publications

Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

Social retrieval of music content in multi-user performance

Design and Implementation of a Flexible Hand Gesture Command Interface for Games Based on Computer Vision

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