Body Motion Analysis for Multi-modal Identity Verification

Williams, George; Taylor, Graham W.; Smolskiy, Kirill; Bregler, Christoph

doi:10.1109/icpr.2010.538

Cited by 7 publications

(10 citation statements)

References 13 publications

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“…We also describe the design and deployment of HITs. This MT-based system complements our other research efforts that use fully automatic vision and statistical learning approaches to extract similarly meaningful representations from video data [20].…”

Section: Introductionmentioning

confidence: 82%

“…Our efforts concerned processing all videos with automatic motion estimation and machine learning techniques (similar to bag-offeature representations that do not need explicit labels, only coarse motion-style targets) and we got promising results for various recognition and clustering tasks [19,20].…”

Section: Case Studymentioning

confidence: 99%

“…With the domain of human gesture tracking, there is a wide variety of approaches available that can automatically track and recognize human gestures and human motion (including our own work [19,20]). It is beyond the scope of this paper to review all related tracking techniques and we refer to [4] for a survey.…”

Section: Gesture Trackingmentioning

confidence: 99%

“…We previously developed a prototype system that can learn statistical models of individual "motion style" for subjects that are engaged in speaking actions. We have successfully applied this to a variety of tasks, including automated identification of personal speaking style, and to more general body motion styles [20]. That specific system uses no explicit representation of hand motion, or gesture types.…”

Section: Introductionmentioning

confidence: 99%

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Hands by hand: Crowd-sourced motion tracking for gesture annotation

Spiro

Taylor

Williams

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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We describe a method for using crowd-sourced labor to track motion and ultimately annotate gestures of humans in video. Our chosen platform for deployment, Amazon Mechanical Turk, divides labor into HITs (Human Intelligence Tasks). Given the informational density of video, our task is potentially larger than a traditional HIT that involves processing a block of text or a single image. We exploit redundancies in video data in such a way that workers' efforts can be multiplied in effect. In the end, a fraction of frames need to be annotated by hand, but we can still achieve complete coverage of all video frames. This is achieved with a combination of HITs using a novel user interface, combined with automatic techniques such as template tracking and affinity propagation clustering. We show in a case study how we can annotate a video database of political speeches with 2D positions and 3D hand pose configurations. This data is then used for some preliminary analytical tasks.

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Section: Introductionmentioning

confidence: 82%

Section: Case Studymentioning

confidence: 99%

Section: Gesture Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hands by hand: Crowd-sourced motion tracking for gesture annotation

Spiro

Taylor

Williams

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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“…To address this important limitations, a mobile EEG-based brain imaging (MoBI) approach may be a valuable tool for recording and analyzing what the brain and the body do during the production of expressive movements, what the brain and the body experience, and what or how the brain self-organizes while movements of physical virtuosity are modified by expressive qualities that communicate emotional tone and texture—the basic language of human interactions. These expressive patterns are unique to each person, and we organize them in such particular ways that they become markers for our identities, even at great distances and from behind (Williams et al, 2008 ; Hodzic et al, 2009 ; Ramsey et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Neural decoding of expressive human movement from scalp electroencephalography (EEG)

Cruz-Garza¹,

Hernandez²,

Nepaul³

et al. 2014

Front. Hum. Neurosci.

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Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities (“Neutral”), non-expressive movements while thinking about specific expressive qualities (“Think”), and enacted expressive movements (“Do”). The expressive movement qualities that were used in the “Think” and “Do” actions consisted of a sequence of eight Laban Effort qualities as defined by LMA—a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2–4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.

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Predicting privileged information for height estimation

Sarafianos

Nikou

Kakadiaris

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology. The actual values of the anthropometric measurements are difficult to estimate accurately using state-of-the-art computer vision algorithms. Hence, we use ratios of anthropometric measurements as features. Since many anthropometric measurements are not available at test time in real-life scenarios, we employ a learning using privileged information (LUPI) framework in a regression setup. Instead of using the LUPI paradigm for regression in its original form (i.e., -SVR+), we train regression models that predict the privileged information at test time. The predictions are then used, along with observable features, to perform height estimation. Once the height is estimated, a mapping to classes is performed. We demonstrate that the proposed approach can estimate the height better and faster than the -SVR+ algorithm and report results for different genders and quartiles of humans.

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Body Motion Analysis for Multi-modal Identity Verification

Cited by 7 publications

References 13 publications

Hands by hand: Crowd-sourced motion tracking for gesture annotation

Hands by hand: Crowd-sourced motion tracking for gesture annotation

Neural decoding of expressive human movement from scalp electroencephalography (EEG)

Predicting privileged information for height estimation

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