Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey
TL;DR: In this article, the authors discuss work done in the area of hand gesture recognition where focus is on the intelligent approaches including soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms etc.
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Abstract: Hand gestures recognition (HGR) is one of the main areas of research for the engineers, scientists and bioinformatics. HGR is the natural way of Human Machine interaction and today many researchers in the academia and industry are working on different application to make interactions more easy, natural and convenient without wearing any extra device. HGR can be applied from games control to vision enabled robot control, from virtual reality to smart home systems. In this paper we are discussing work done in the area of hand gesture recognition where focus is on the intelligent approaches including soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms etc. The methods in the preprocessing of image for segmentation and hand image construction also taken into study. Most researchers used fingertips for hand detection in appearance based modeling. Finally the comparison of results given by different researchers is also presented.
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Figures
![Fig 4: Hand Gesture Recognition process form video [52]](/figures/figure4-1-54gq3porh81f.png)
Fig 4: Hand Gesture Recognition process form video [52] ![Fig 1: Chinese sign language [41]](/figures/figure1-1-2ezv09b6iz57.png)
Fig 1: Chinese sign language [41] ![Fig 5: Hand (a) coordinates and (b) eigen vectors [58]](/figures/figure5-1-1viazvqvsvbi.png)
Fig 5: Hand (a) coordinates and (b) eigen vectors [58] ![Fig 6: Result of finger extraction using grayscale morphology operators and object analysis [12] which work for bended finger also, but with a lower accuracy 10-20%.](/figures/figure6-1-ssicv1voqyc9.png)
Fig 6: Result of finger extraction using grayscale morphology operators and object analysis [12] which work for bended finger also, but with a lower accuracy 10-20%. ![Fig 2: (a) hand model (b) local coordinate frames on the joint position for middle finger [62]](/figures/figure2-1-26zljhk4fx3y.png)
Fig 2: (a) hand model (b) local coordinate frames on the joint position for middle finger [62] ![Fig 3: SGONG network working (a) start with two points (b) growing stage with 45 neurons (c) output with 83 neurons (d) hand gesture (e) only raised fingers would be counted [59]](/figures/figure3-1-31d4rmsh6szc.png)
Fig 3: SGONG network working (a) start with two points (b) growing stage with 45 neurons (c) output with 83 neurons (d) hand gesture (e) only raised fingers would be counted [59]
![Fig 4: Hand Gesture Recognition process form video [52]](/figures/figure4-1-54gq3porh81f.webp)
![Fig 1: Chinese sign language [41]](/figures/figure1-1-2ezv09b6iz57.webp)
![Fig 5: Hand (a) coordinates and (b) eigen vectors [58]](/figures/figure5-1-1viazvqvsvbi.webp)
![Fig 6: Result of finger extraction using grayscale morphology operators and object analysis [12] which work for bended finger also, but with a lower accuracy 10-20%.](/figures/figure6-1-ssicv1voqyc9.webp)
![Fig 2: (a) hand model (b) local coordinate frames on the joint position for middle finger [62]](/figures/figure2-1-26zljhk4fx3y.webp)
![Fig 3: SGONG network working (a) start with two points (b) growing stage with 45 neurons (c) output with 83 neurons (d) hand gesture (e) only raised fingers would be counted [59]](/figures/figure3-1-31d4rmsh6szc.webp)
Citations
Vision based hand gesture recognition for human computer interaction: a survey
TL;DR: An analysis of comparative surveys done in the field of gesture based HCI and an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters are provided.
Max-pooling convolutional neural networks for vision-based hand gesture recognition
Jawad Nagi,Frederick Ducatelle,Gianni A. Di Caro,Dan Ciresan,Ueli Meier,Alessandro Giusti,Farrukh Nagi,Jürgen Schmidhuber,Luca Maria Gambardella +8 more
- 01 Nov 2011
TL;DR: This work uses a state-of-the-art big and deep neural network combining convolution and max-pooling for supervised feature learning and classification of hand gestures given by humans to mobile robots using colored gloves.
A review of hand gesture and sign language recognition techniques
TL;DR: A thorough review of state-of-the-art techniques used in recent hand gesture and sign language recognition research, suitably categorized into different stages: data acquisition, pre-processing, segmentation, feature extraction and classification.
563
Dual-Hand Detection for Human–Robot Interaction by a Parallel Network Based on Hand Detection and Body Pose Estimation
TL;DR: Experimental results verify that the parallel deep neural network can effectively improve the accuracy of hand detection and distinguish between the right and left hands effectively.
A systematic literature review on vision based gesture recognition techniques
Ahmad Sami Al-Shamayleh,Rodina Ahmad,Mohammad A. M. Abushariah,Khubaib Amjad Alam,Nazean Jomhari +4 more
TL;DR: The results reveal that among the VBR techniques, HGR is a predominant and highly focused area of research, and research focus is also found to be converging towards sign language recognition.
122
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Translation and scale-invariant gesture recognition in complex scenes
Alexandra Stefan,Vassilis Athitsos,Jonathan Alon,Stan Sclaroff +3 more
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TL;DR: The key idea is to integrate a face detection module into the gesture recognition system, and use the face location and size to make gesture recognition invariant to scale and translation.
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The search for a safer driver interface: a review of gesture recognition human machine interface
TL;DR: An alternative system is being developed that an automotive gesture recognition system using electric field sensors can achieve the same safety benefits as vision-based systems, at lower cost, without the same technical difficulties.
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A Framework for 3D Hand Tracking and Gesture Recognition using Elements of Genetic Programming
A. El-Sawah,Chris Joslin,N.D. Georganas,Emil M. Petriu +3 more
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TL;DR: A framework for 3D hand tracking and dynamic gesture recognition using a single camera that utilizes elements of soft computing to resolve the ambiguity inherent in vision-based tracking by producing a fuzzy hand posture output by the hand tracking module and feeding back potential posture hypothesis from the gesture recognition module.
36
Dynamic Hand Gesture Tracking and Recognition for Real-Time Immersive Virtual Object Manipulation
Gan Lu,Lik-Kwan Shark,Geoff Hall,Ulrike Zeshan +3 more
- 07 Sep 2009
TL;DR: The proposed human-computer interaction system is shown to be able to automatically track and recognise a number of simple hand gestures and is demonstrated through the five basic object manipulation tasks involving selection, release, translation, rotation and scaling of a 3D virtual cube.
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