Hand gesture recognition using DWT and F‐ratio based feature descriptor. Issue 10 (1st October 2018)

• Record Type: Journal Article
• Authors: Sahoo, Jaya Prakash; Ari, Samit; Ghosh, Dipak Kumar
• Is Part Of: IET image processing. Volume 12:Issue 10(2018)
• Page Start: 1780
• Page End: 1787
• Publication Date: 2018-10-01
• Subjects: gesture recognition -- palmprint recognition -- feature extraction -- discrete wavelet transforms -- cameras -- object detection -- compensation -- support vector machines -- image classification; F‐ratio based feature descriptor -- DWT -- vision based static hand gesture recognition system -- Web camera -- illumination compensation -- hand region detection -- image resize -- discrete wavelet transform -- Fisher ratio -- feature extraction technique -- hand gesture classification -- gesture vocabulary -- linear support vector machine -- standard public datasets -- complex background dataset -- American sign language hand alphabets -- ASL hand alphabets -- image browsing; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.1312
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This study demonstrates the development of vision based static hand gesture recognition system using web camera in real‐time applications. The vision based static hand gesture recognition system is developed using the following steps: preprocessing, feature extraction and classification. The preprocessing stage consists of illumination compensation, segmentation, filtering, hand region detection and image resize. This study proposes a discrete wavelet transform (DWT) and Fisher ratio ( F ‐ratio) based feature extraction technique to classify the hand gestures in an uncontrolled environment. This method is not only robust towards distortion and gesture vocabulary, but also invariant to translation and rotation of hand gestures. A linear support vector machine is used as a classifier to recognise the hand gestures. The performance of the proposed method is evaluated on two standard public datasets and one indigenously developed complex background dataset for recognition of hand gestures. All above three datasets are developed based on American Sign Language (ASL) hand alphabets. The experimental result is evaluated in terms of mean accuracy. Two possible real‐time applications are conducted, one is for interpretation of ASL sign alphabets and another is for image browsing.