Face Tracking Performance in Head Gesture Recognition System 
Rushikesh Bankar1, Suresh Salankar2
1Rushikesh Bankar*, Department of Electronics Engineering, G H Raisoni College of Engineering, Nagpur, India.
2Dr. Suresh Salankar, Department of Electronics & Telecommunication Engineering, G H Raisoni College of Engineering, Nagpur, India.
Manuscript received on June 01, 2020. | Revised Manuscript received on June 08, 2020. | Manuscript published on June 30, 2020. | PP: 1096-1099 | Volume-9 Issue-5, June 2020. | Retrieval Number: E1043069520/2020©BEIESP | DOI: 10.35940/ijeat.E1043.069520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Camshift, Camshift with BLBP, Face Detection, Face Tracking