Open AccessHand gesture recognition using DWT and F ‐ratio based feature descriptor
Author(s) -
Sahoo Jaya Prakash,
Ari Samit,
Ghosh Dipak Kumar
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.1312
Subject(s) - computer science , gesture , artificial intelligence , gesture recognition , preprocessor , feature extraction , pattern recognition (psychology) , support vector machine , computer vision , segmentation , classifier (uml) , sign language , speech recognition , linear discriminant analysis , philosophy , linguistics
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.