Stable Face Features Tracking under Unconstrained Condition

A method for tracking facial feature points stably is proposed to complete the facial feature points tracking accurately.First,the online update reference texture model is combined with the original active appearance model(AAM).Second,use the subspace update mechanism.The AAM texture model and the reference model are updated via the incremental learning method.Then the online reference appearance model(ORAM) fitting algorithm based on simultaneously inverse compositional is designed.To reduce the cumulative error,texture subspace reset mechanism is introduced based on the first stably tracked frames.Finally,face features tracking is completed.Compared with other AAM algorithms that require a large amount of training data,ORAM need no training data.It is proved that this method can complete the face tracking accurately and quickly in different posture,facial expression and illumination conditions.