Open AccessFacial Feature Tracking Using Efficient Particle Filter and Active Appearance Model
Author(s) -
Durkhyun Cho,
Sanghoon Lee,
Il Hong Suh
Publication year - 2014
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/58761
Subject(s) - particle filter , computer science , tracking (education) , artificial intelligence , active appearance model , computer vision , feature (linguistics) , cluster analysis , filter (signal processing) , active contour model , face (sociological concept) , particle (ecology) , image (mathematics) , image segmentation , psychology , pedagogy , linguistics , philosophy , social science , sociology , oceanography , geology
For natural human-robot interaction, the location and shape of facial features in a real environment must be identified. One robust method to track facial features is by using a particle filter and the active appearance model. However, the processing speed of this method is too slow for utilization in practice. In order to improve the efficiency of the method, we propose two ideas: (1) changing the number of particles situationally, and (2) switching the prediction model depending upon the degree of the importance of each particle using a combination strategy and a clustering strategy. Experimental results show that the proposed method is about four times faster than the conventional method using a particle filter and the active appearance model, without any loss of performance