Hand gesture recognition with depth data

Dominio, Fabio; Donadeo, Mauro; Marin, Giulio; Zanuttigh, Pietro; Cortelazzo, G.M.

doi:10.1145/2510650.2510651

Cited by 36 publications

(15 citation statements)

References 18 publications

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“…The gesture identification of the GestureKeeper employs two RQA metrics, namely, the recurrence rate (RR) and the transitivity (TRA) (see [26] for the definitions), obtained from the y-axis acceleration data, in order to form the feature matrix 1 .…”

Section: A Gesture Identificationmentioning

confidence: 99%

“…In general, hand-gesture recognition systems can be classified in two categories, according to the type of sensors they employ, namely, the camera-and the wearable-based ones. The camera-based systems can achieve high recognition accuracy, but at a relatively high computational cost [1]. The performance of these systems is sensitive in the background, light conditions, and room geometry, and constrained by the field of view of the camera.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments

Sideridis

Zacharakis

Tzagkarakis

et al. 2019

2019 27th European Signal Processing Conference (EUSIPCO)

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This paper introduces and evaluates the Gesture-Keeper, a robust hand-gesture recognition system based on a wearable inertial measurements unit (IMU). The identification of the time windows where the gestures occur, without relying on an explicit user action or a special gesture marker, is a very challenging task. To address this problem, GestureKeeper identifies the start of a gesture by exploiting the underlying dynamics of the associated time series using a recurrence quantification analysis (RQA). RQA is a powerful method for nonlinear timeseries analysis, which enables the detection of critical transitions in the system's dynamical behavior. Most importantly, it does not make any assumption about the underlying distribution or model that governs the data. Having estimated the gesture window, a support vector machine is employed to recognize the specific gesture. Our proposed method is evaluated by means of a small-scale pilot study at FORTH and demonstrated that GestureKeeper can identify correctly the start of a gesture with a 87% mean balanced accuracy and classify correctly the specific hand-gesture with a mean accuracy of over 96%. To the best of our knowledge, GestureKeeper is the first automatic handgesture identification system based only on accelerometer. The performance analysis reveals the predictive power of the features and the system's robustness in the presence of additive noise. We also performed a sensitivity analysis to examine the impact of various parameters and a comparative analysis of different classifiers (SVM, random forests). Most importantly, the system can be extended to incorporate a large dictionary of gestures and operate without further calibration for a new user.Index Terms-Hand-gesture identification and recognition, inertial measurement unit, support vector machine, recurrence quantification analysis

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Section: A Gesture Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments

Sideridis

Zacharakis

Tzagkarakis

et al. 2019

2019 27th European Signal Processing Conference (EUSIPCO)

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show abstract

“…This problem has been tackled by comparing the histograms of the distance of hand edge points from the hand center in order to recognize the gestures in various works like [30,31] and [11]. This technique leads to good results even if a precise alignment of the histograms is needed for optimal performances.…”

Section: Related Workmentioning

confidence: 99%

Head-mounted gesture controlled interface for human-computer interaction

Memo

Zanuttigh

2016

Multimed Tools Appl

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This paper proposes a novel human-computer interaction system exploiting gesture recognition. It is based on the combined usage of an head-mounted display and a multi-modal sensor setup including also a depth camera. The depth information is used both to seamlessly include augmented reality elements into the real world and as input for a novel gesture-based interface. Reliable gesture recognition is obtained through a real-time algorithm exploiting novel feature descriptors arranged in a multi-dimensional structure fed to an SVM classifier. The system has been tested with various augmented reality applications including an innovative human-computer interaction scheme where virtual windows can be arranged into the real world observed by the user

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“…Color data can be combined with the depth in order to improve the accuracy and better segment the hand from the arm, as in [4]. Other approaches also exploit some physical aids, e.g., in [25], after thresholding the depth map, a black bracelet on the gesturing hand's wrist is recognized in the color image for an accurate hand detection.…”

Section: Hand Extractionmentioning

confidence: 99%

“…Here, a brief description of the approach of [5] and [4], that in turn extends the scheme of [25], is given.…”

Section: Distance Featuresmentioning

confidence: 99%

Feature Descriptors for Depth-Based Hand Gesture Recognition

Dominio¹,

Marin²,

Piazza³

et al. 2014

Computer Vision and Machine Learning With RGB-D Sensors

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Depth data acquired by consumer depth cameras provide a very informative description of the hand pose that can be exploited for accurate gesture recognition. A typical hand gesture recognition pipeline requires to identify the hand, extract some relevant features and exploit a suitable machine learning technique to recognize the performed gesture. This chapter deals with the recognition of static poses. It starts by describing how the hand can be extracted from the scene exploiting depth and color data. Then several different features that can be extracted from the depth data are presented. Finally, a multi-class support vector machines (SVM) classifier is applied to the presented features in order to evaluate the performance of the various descriptors.

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Hand gesture recognition with depth data

Cited by 36 publications

References 18 publications

GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments

GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments

Head-mounted gesture controlled interface for human-computer interaction

Feature Descriptors for Depth-Based Hand Gesture Recognition

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