Open AccessSIFT and SURF features based classification of yoga hand mudras using machine learning techniques
Author(s) -
S. Abarna,
V. Rathikarani,
P. Dhanalakshmi
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns1.4946
Subject(s) - scale invariant feature transform , support vector machine , artificial intelligence , machine learning , consciousness , computer science , harmony (color) , random forest , psychology , feature extraction , art , neuroscience , visual arts
Yoga is an unique spiritual discipline of self-development and self-realization that teaches us how to live our lives to the fullest. Yoga's integrative approach brings deep harmony and unwavering balance to body and mind to awaken our dormant capacity for higher consciousness, which is the true purpose of human evolution. The numerous documented physical and mental benefits of yoga have played a large part in the interest in yoga. Due to a lack of datasets and thus the necessity to identify mudra in real time, distinguishing yoga hand mudras seems to be a tough undertaking. The yoga hand mudras are used as input in the proposed study, and the two components Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) are extracted, followed by classification utilising machine learning techniques including Support Vector Machine (SVM) and Random Forest. By comparing the experimental results the performance of SIFT with SVM yields better results.