Open AccessFace Tracking Performance in Head Gesture Recognition System
Author(s) -
Rushikesh T. Bankar,
Suresh Salankar
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e1043.069520
Subject(s) - artificial intelligence , computer science , computer vision , facial motion capture , gesture recognition , feature extraction , adaboost , gesture , robustness (evolution) , pattern recognition (psychology) , facial recognition system , face detection , tracking system , tracking (education) , face (sociological concept) , three dimensional face recognition , support vector machine , kalman filter , psychology , pedagogy , biochemistry , chemistry , social science , sociology , gene
This paper describes the comparative analysis of different face tracking methods in the head gesture recognition system. The major constraints of head gesture recognition system, i.e. face detection, feature extraction, tracking, and recognition are explained. We used adaboost algorithm for detection, and Camshift algorithm for tracking with different feature extraction methods. We performed extensive experimentations and presented a comparative analysis of tracking performance of head gesture recognition system under cluttered backgrounds, shadow and sunshine conditions. Experimental results show the robustness in face detection, tracking and direction recognition of the proposed method.