Analysis of Various Features of Hand Gestures using Leap Motion Controller for Indian Sign Language Interpretation
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2018 by IJETT Journal|
|Year of Publication : 2018|
|Authors : Archana Ghotkar, Pujashree Vidap
|DOI : 10.14445/22315381/IJETT-V61P203|
MLA Style: Archana Ghotkar, Pujashree Vidap"Analysis of Various Features of Hand Gestures using Leap Motion Controller for Indian Sign Language Interpretation" International Journal of Engineering Trends and Technology 61.1 (2018): 14-19.
APA Style:Archana Ghotkar, Pujashree Vidap, (2018). Analysis of Various Features of Hand Gestures using Leap Motion Controller for Indian Sign Language InterpretationInternational Journal of Engineering Trends and Technology, 61(1), 14-19.
Hand gesture recognition for sign language interpretation is an active research currently going on. As a computer vision application, varieties of sensors/cameras with notable features are available for capturing live gesture. In this paper, real time hand gesture recognition has been done with Leap motion sensor. The main objective of this research is to do analysis of various derived (hand) features provided by leap motion with different classifier and select notable features with tested classifier for the further study. Indian sign language (ISL) dataset of alphabets and numbers are considered for performance analysis. Here, total 68 features for both hands (34 for each hand) are derived and tested with nearest neighbourhood, Logistic regression, Support Vector Machine (SVM) and nearest mean classifier. The fusion vector of 68 features are created and tested with different classifier to check the performance. Result shows that, SVM classifier giving better result of 96.19% for ISL alphabets and 100 % for ISL numbers with fusion vector. The detailed analysis shows that selection of hand orientation features instead of distance features are also a good choice for hand gesture recognition.
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Indian Sign Language, 3D Leap Motion sensor, Pattern recognition, k-nearest neighbor, Logistic regression, Support vector machine, Nearest mean classifier.