NDLI: Optimisation of both classifier and fusion based feature set for static American sign language recognition

Content Provider	IET Digital Library
Author	C., Arun Gopikakumari, R.
Abstract	Sign language recognition becomes a popular research field in human–computer interaction. Attention on hand signal analysis helps to make easy communication among computer and human for information sharing. Major focus of the gesture recognition system is to identify and recognise various gestures, by a computer. This study introduces optimisation of both classifier and feature set for static American sign language recognition. Initially, the hand part is segmented from other parts of the image through effective edge and skin colour detection. Thereafter, robust features are obtained using discrete cosine transform, Zernike moment, scale-invariant feature transform, speeded-up robust features, histogram of oriented gradients and binary object features from the segmented hand image. From these extracted features, an optimal feature set is selected by social ski driver optimisation algorithm. Deep Elman recurrent neural network classifier is then introduced for recognition purpose. Optimisation is performed on feature sets, derived by fusion of features obtained from the above methods, based on precision, accuracy, F-measure and recall. Finally, optimised feature set and best classifier are used to recognise the hand gesture for classification purpose. The performance of this proposed method is evaluated and compared with existing literature.
Starting Page	2101
Ending Page	2109
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 10, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0195
Journal	IET Image Processing
Publisher Date	2020-04-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Binary Object Features Computer Vision And Image Processing Technique Deep Elman Recurrent Neural Network Classifier Discrete Cosine Transform Feature Extraction Fusion Based Feature Set Gesture Recognition Gesture Recognition System Hand Gesture Hand Signal Analysis Histogram of Oriented Gradients Human-computer Interaction Image Classification Image Colour Analysis Image Fusion Image Recognition Image Segmentation Integral Transforms in Numerical Analysis Interpolation And Function Approximation Neural Computing Technique Numerical Analysis Optimal Feature Set Optimisation Optimisation Technique Optimised Feature Set Recognition Purpose Recurrent Neural Nets Scale-invariant Feature Transform Segmented Hand Image Sensor Fusion Skin Colour Detection Social Ski Driver Optimisation Algorithm Static American Sign Language Recognition Zernike Moment Zernike Polynomials
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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