Hand Gesture Recognition: A Literature Review

Khan, Rafiqul Zaman

doi:10.5121/ijaia.2012.3412

Cited by 112 publications

(29 citation statements)

References 15 publications

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“…Choosing Gaussian elimination procedure to rewrite the upper equation in more solvable in = (10) where (11) solving X to find the coefficients A, B in = * (12) The outcome would be the coefficients a 0 , a 1 , a 2 . We follow the similar procedure to find the coefficient sets of the remaining landmark points.…”

Section: Regression Curvesmentioning

confidence: 99%

Mixtures of Regression Curve Models for Arabic Character Recognition

Al-Shaher¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

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In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We proceed then, by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.

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Section: Regression Curvesmentioning

confidence: 99%

Mixtures of Regression Curve Models for Arabic Character Recognition

Al-Shaher¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

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“…S. Nagarajan [13] et al proposes a static hand gesture recognition system for American Sign Language using Edge Oriented Histogram (EOH) features and multiclass SVM. Rafiqul Zaman Khan [14] discusses key issues of hand gesture recognition system with challenges of gesture system. He also present review of methods of recent postures and gestures recognition system.…”

Section: Sign Language Alphabets Recognitionmentioning

confidence: 99%

Sign Language Alphabets Recognition Using Wavelet Transform

2015

International Academy of Engineers (IA-E) August 25-26, 2015 Kuala Lumpur, Malaysia

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Bridging communication gap between the common man and the hearing impaired people is a big challenge. Thus lot of research is being done in the area of sign language recognition to come-up with the optimum algorithm or solution for cent percent recognition of signs. Till now numbers of techniques are being developed. This article explains wavelet feature based method to recognize the 24 static-image based alphabets A to Z (excluding dynamic alphabets J and Z) of American Sign Language (ASL). This method extracts the wavelet feature vectors of the images. Further neural network is used for the classification of these alphabets. This method is qualified to provide an average recognition rate of 97.47 percent at fourth level decomposition of wavelet features.

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“…With the ever increasing prevalence of human-computer interaction tasks, it is no surprise that gesture recognition remains a major topic of research in computer vision [1,2,3]. With any vision task occlusion means a loss of data, and thus degraded performance.…”

Section: Introductionmentioning

confidence: 99%

Duodepth: Static Gesture Recognition Via Dual Depth Sensors

Chugunov

Zakhor

2019

2019 IEEE International Conference on Image Processing (ICIP)

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Static gesture recognition is an effective non-verbal communication channel between a user and their devices; however many modern methods are sensitive to the relative pose of the user's hands with respect to the capture device, as parts of the gesture can become occluded. We present two methodologies for gesture recognition via synchronized recording from two depth cameras to alleviate this occlusion problem. One is a more classic approach using iterative closest point registration to accurately fuse point clouds and a single PointNet architecture for classification, and the other is a dual Point-Net architecture for classification without registration. On a manually collected data-set of 20,100 point clouds we show a 39.2% reduction in misclassification for the fused point cloud method, and 53.4% for the dual PointNet, when compared to a standard single camera pipeline.

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Hand Gesture Recognition: A Literature Review

Abstract: ABSTRACT

Cited by 112 publications

References 15 publications

Mixtures of Regression Curve Models for Arabic Character Recognition

Mixtures of Regression Curve Models for Arabic Character Recognition

Sign Language Alphabets Recognition Using Wavelet Transform

Duodepth: Static Gesture Recognition Via Dual Depth Sensors

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