Hand gesture modelling and recognition involving changing shapes and trajectories, using a Predictive EigenTracker

Patwardhan, Kaustubh Srikrishna; Roy, Sumantra Dutta

doi:10.1016/j.patrec.2006.04.002

Cited by 41 publications

(15 citation statements)

References 17 publications

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“…The vision based detection of head movement is relatively easier than the detection of gestures and the whole body action. Because the shape of head is rather simple and unchanging, while the geometry of hands and the whole body are much more complex and changeable [33], and the self-occlusion problem is unavoidable in most applications. Image display is one of the most common used output channels and can provide a great amount of information to users.…”

Section: Input and Output Modalitiesmentioning

confidence: 99%

A Review of Multimodal Interaction

Liang¹,

Liang²,

Xue-qian³

et al. 2016

Advances in Computer Science Research

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Abstract-Due to the contribution to a more natural and efficient human-computer interaction style, multimodal interactive systems are becoming more and more commonplace and cover a wide range of applicants. Therefore, there seems to be an urgent need for an introduction to related concepts of multimodal interaction and an analytical review of the-state-of-the-art. In this paper, we try to present a brief review of some of the significant aspects in multimodal interaction. At first, the definition and advantages of multimodal systems are discussed, and then a brief development history of multimodal interaction is introduced. Next, we touch on the recent development in the breadth of work on input/output modalities and fusion engine. Finally, we provide a personal outlook for the research of multimodal interaction. It is our hope for this paper to provide a picture of the contemporary state of multimodal interaction research and a meaningful outlook for this research field.

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Section: Input and Output Modalitiesmentioning

confidence: 99%

A Review of Multimodal Interaction

Liang¹,

Liang²,

Xue-qian³

et al. 2016

Advances in Computer Science Research

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“…Results show a recognition rate of 92% on 33 signs. Patwardhan and Roy (2007) uses a predictive Eigen-Tracker to track the changing appearance of a moving hand. The algorithm obtains the affine transforms of the image frames and projects the image to the eigenspace.…”

Section: Related Workmentioning

confidence: 99%

A person independent system for recognition of hand postures used in sign language

Kelly

McDonald

Markham

2010

Pattern Recognition Letters

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a b s t r a c tWe present a novel user independent framework for representing and recognizing hand postures used in sign language. We propose a novel hand posture feature, an eigenspace Size Function, which is robust to classifying hand postures independent of the person performing them. An analysis of the discriminatory properties of our proposed eigenspace Size Function shows a significant improvement in performance when compared to the original unmodified Size Function.We describe our support vector machine based recognition framework which uses a combination of our eigenspace Size Function and Hu moments features to classify different hand postures. Experiments, based on two different hand posture data sets, show that our method is robust at recognizing hand postures independent of the person performing them. Our method also performs well compared to other user independent hand posture recognition systems.

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“…2 Origin hand shapes translated in all directions using combinations of 2, 4 and 6 pixels. 3 9 rotations are used in the yaw direction as this is the direction that contains most significant deviation. These rotations are 3 degrees apart covering a total pitch of 24 degrees.…”

Section: Static Hand Shape Classificationmentioning

confidence: 99%

“…Chen et al [2] developed a dynamic gesture recognition system using Hidden Markov Models (HMMs). Patwardhan et al [3] recently introduced a system based on a predictive eigentracker to track the changing appearance of a moving hand. Kadir et al [4] describe a technique to recognize sign language gestures using a set of discrete features to describe position of the hands relative to each other, position of the hands relative to other body locations, movement of the hand, shape of the hand.…”

Section: Introductionmentioning

confidence: 99%

Real Time Hand Gesture Recognition Including Hand Segmentation and Tracking

Coogan

Awad

Han

et al. 2006

Advances in Visual Computing

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Abstract. In this paper we present a system that performs automatic gesture recognition. The system consists of two main components: (i) A unified technique for segmentation and tracking of face and hands using a skin detection algorithm along with handling occlusion between skin objects to keep track of the status of the occluded parts. This is realized by combining 3 useful features, namely, color, motion and position. (ii) A static and dynamic gesture recognition system. Static gesture recognition is achieved using a robust hand shape classification, based on PCA subspaces, that is invariant to scale along with small translation and rotation transformations. Combining hand shape classification with position information and using DHMMs allows us to accomplish dynamic gesture recognition.

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Hand gesture modelling and recognition involving changing shapes and trajectories, using a Predictive EigenTracker

Cited by 41 publications

References 17 publications

A Review of Multimodal Interaction

A Review of Multimodal Interaction

A person independent system for recognition of hand postures used in sign language

Real Time Hand Gesture Recognition Including Hand Segmentation and Tracking

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