Facial Features Tracking Using Auxiliary Particle Filtering and Observation Model Based on Bhattacharyya Distance
Fatemeh Shirinzadeh, Hadi Seyedarabi, Ali Aghagolzadeh · International Journal of Computer Theory and Engineering · 2012
Recently, particle filtering has become an effective algorithm for facial feature tracking. One problem with particle filtering is that as the dimensionality of the state space increases, a large number of particles that are propagated from the previous time are wasted in area where they have low observation probability, hence a very large number of particles are necessary to track the state and, as a result the complexity increases and the speed of algorithm reduces. In this research, auxiliary particle filter with factorized likelihoods is used in order to overcome this problem. In a tracking approach, the estimated state is updated by incorporating the new observations. Therefore an observation model is needed. In this paper a novel color-based observation model that is invariant to changes in illumination intensity is proposed. The proposed observation model employs the Bhattacharyya distance to update a prior distribution calculated by the particle filter. In this paper experimentally is showed that the proposed algorithm clearly outperforms multiple independent template tracking.