On an algorithm for Vision-based hand gesture recognition. Issue 4 (April 2016)

• Record Type: Journal Article
• Authors: Ghosh, Dipak; Ari, Samit
• Is Part Of: Signal, image and video processing. Volume 10:Issue 4(2016)
• Page Start: 655
• Page End: 662
• Publication Date: 2016-04
• Subjects: American sign language (ASL) hand alphabet -- Combined feature -- Genetic algorithm (GA) -- Hand gesture recognition -- Least-mean-square (LMS) algorithm -- Radial basis function (RBF) neural network; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-015-0790-4
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract A vision-based static hand gesture recognition method which consists of preprocessing, feature extraction, feature selection and classification stages is presented in this work. The preprocessing stage involves image enhancement, segmentation, rotation and filtering. This work proposes an image rotation technique that makes segmented image rotation invariant and explores a combined feature set, using localized contour sequences and block-based features for better representation of static hand gesture. Genetic algorithm is used here to select optimized feature subset from the combined feature set. This work also proposes an improved version of radial basis function (RBF) neural network to classify hand gesture images using selected combined features. In the proposed RBF neural network, the centers are automatically selected using k-means algorithm and estimated weight matrix is recursively updated, utilizing least-mean-square algorithm for better recognition of hand gesture images. The comparative performances are tested on two indigenously developed databases of 24 American sign language hand alphabet.