Multimodal desktop interaction: The face - object - gesture - voice example

Vidakis, Nikolas; Vlasopoulos, Anastasios; Kounalakis, Tsampikos; Varchalamas, Petros; Dimitriou, Michalis; Kalliatakis, Gregory; Syntychakis, Efthimios; Christofakis, John; Triantafyllidis, Georgios

doi:10.1109/icdsp.2013.6622782

Cited by 4 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interpreting the user's explicit intention, which contains valuable information, is vital in developing efficient human computer interfaces. In conventional human computer interface (HCI) and human robot interaction (HRI) environments, the user intention such as 'copy this file' or 'create a folder' can be explicitly conveyed through a keyboard and a computer mouse [11,12], which can be easily interpreted. The process of data visualisation is suitable for externalising the facts and enabling people to understand and manipulate the results at a higher level.…”

Section: Introductionmentioning

confidence: 99%

Bringing Depth Data Alive: Conceive Human Intention through Web Visualisations of Head Pose and Emotion Changes <sup>&dagger;</sup>

Kalliatakis¹,

Stergiou²,

Vidakis³

2017

Preprint

View full text Add to dashboard Cite

Affective computing in general and human activity and intention analysis in particular, is a rapidly growing field of research. Head pose and emotion changes, present serious challenges when applied to player's training and ludology 1 experience in serious games or analysis of customer satisfaction regarding broadcast and web services or monitoring a driver's attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need of generating comprehensible visual representations from different sets of data, in this paper we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor).

show abstract

Section: Introductionmentioning

confidence: 99%

Bringing Depth Data Alive: Conceive Human Intention through Web Visualisations of Head Pose and Emotion Changes <sup>&dagger;</sup>

Kalliatakis¹,

Stergiou²,

Vidakis³

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Interpreting the user's explicit intention, which contains valuable information, is vital in developing efficient human computer interfaces. In conventional human computer interface (HCI) and human robot interaction (HRI) environments, the user intention such as "copy this file" or "create a folder" can be explicitly conveyed through a keyboard and a computer mouse [18,19], which can be easily interpreted. The process of data visualisation is suitable for externalising the facts and enabling people to understand and manipulate the results at a higher level.…”

Section: Introductionmentioning

confidence: 99%

Conceiving Human Interaction by Visualising Depth Data of Head Pose Changes and Emotion Recognition via Facial Expressions

2017

View full text Add to dashboard Cite

Affective computing in general and human activity and intention analysis in particular comprise a rapidly-growing field of research. Head pose and emotion changes present serious challenges when applied to player's training and ludology experience in serious games, or analysis of customer satisfaction regarding broadcast and web services, or monitoring a driver's attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests were selected in order to rapidly and accurately estimate head pose changes in an unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation (JSON)) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need to generate comprehensible visual representations from different sets of data, in this paper, we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor).

show abstract

User Intent for Virtual Environments

Cheng

Zhang

et al. 2016

Recent Developments in Intelligent Systems and Interactive Applications

View full text Add to dashboard Cite

Multimodal desktop interaction: The face - object - gesture - voice example

Cited by 4 publications

References 27 publications

Bringing Depth Data Alive: Conceive Human Intention through Web Visualisations of Head Pose and Emotion Changes <sup>&dagger;</sup>

Bringing Depth Data Alive: Conceive Human Intention through Web Visualisations of Head Pose and Emotion Changes <sup>&dagger;</sup>

Conceiving Human Interaction by Visualising Depth Data of Head Pose Changes and Emotion Recognition via Facial Expressions

User Intent for Virtual Environments

Contact Info

Product

Resources

About