Human Computer Interaction for Vision Based Hand Gesture Recognition: A Survey

Hasan, Haitham; Kareem, Sameem Abdul

doi:10.1109/acsat.2012.37

Cited by 43 publications

(21 citation statements)

References 10 publications

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“…In [28]- [30], the authors described a vision-based recognition and how to control a machine. They demonstrated that the vision-based gesture recognition techniques are of utmost importance in designing highly efficient and intelligent human-machine interfaces.…”

Section: B Smart Glovementioning

confidence: 99%

Smart Glove and Hand Gesture-Based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment

Haratiannejadi

Šelmić

2020

IEEE Access

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Section: B Smart Glovementioning

confidence: 99%

Smart Glove and Hand Gesture-Based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment

Haratiannejadi

Šelmić

2020

IEEE Access

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“…In [16] and [17] extensive research surveys have discussed some of the cutting edge technology for vision-based hand gesture recognition techniques that are under research. Some of the key techniques are discussed in subsequent units.…”

Section: S Srividya Anala M Rmentioning

confidence: 99%

Research trends in Hand Gesture Recognition techniques

Srividya,

2020

IJRTE

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Over years' key board and mouse are among the most effective input devices to the computers. Not much improvement has happened for other modes of inputs like voice and gestures. These kinds of inputs explore the limitations caused by the key board and mouse, which needs actual physical contact. With gestures and voice the physical proximity can be little relaxed and can be used by specially-abled people. Exploring effective Human Computer Interactions (HCI) has always been of great interest for development of new techniques and methods. Gestures are considered as more easy, natural, and intuitive method for communicating with systems. Hand recognition and analysis forms the core of these systems. This paper provides comparative study on state of art technologies that work in this area. The different representation, detection and recognition techniques are discussed. The strength and challenges of different techniques are highlighted. The main aim of this research is to summarize the research progress done till date with different technologies for gesture recognition algorithms, identify areas which need further research.

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“…Detailed work has been done on identifying complex and precise hand movements for solutions in applications like surgical simulation and training systems (Liu et al, 2003). In contrast, simpler gestures have been used in computer controlled games, teleconferencing, robotics and augmented vision based solutions (Hasan and Kareem, 2012).…”

Section: Introductionmentioning

confidence: 99%

Adding Model Constraints to CNN for Top View Hand Pose Recognition in Range Images

Tewari

Grandidier²,

Taetz

et al. 2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

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A new dataset for hand-pose is introduced. The dataset includes the top view images of the palm by Time of Flight (ToF) camera. It is recorded in an experimental setting with twelve participants for six hand-poses. An evaluation on the dataset is carried out with a dedicated Convolutional Neural Network (CNN) architecture for Hand Pose Recognition (HPR). This architecture uses a model-layer. The small size model layer creates a funnel shape network which adds a priori knowledge and constrains the network by modelling the degree of freedom of the palm, such that it learns palm features. It is demonstrated that this network performs better than a similar network without the prior added. A two-phase learning scheme which allows training the model on full dataset even when the classification problem is confined to a subset of the classes is described. The best model performs at an accuracy of 92%. Finally, we show the feature transfer capability of the network and compare the extracted features from various networks and discuss usefulness for various applications.

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Human Computer Interaction for Vision Based Hand Gesture Recognition: A Survey

Cited by 43 publications

References 10 publications

Smart Glove and Hand Gesture-Based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment

Smart Glove and Hand Gesture-Based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment

Research trends in Hand Gesture Recognition techniques

Adding Model Constraints to CNN for Top View Hand Pose Recognition in Range Images

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