Failure Detection and Correction for Appearance Based Facial Tracking

The appearance based facial tracking methods, such as active appearance models and candide models, are widely used in intelligent user interface and facial expression recognition. This paper proposes a novel method to detect and correct the failures in appearance based facial tracking. A sparse coding strategy is applied to learn an efficient feature representation for the difference between the warped image and the face template. The features are extracted by directly project the difference image to the space spanned by the dictionary of the sparse coding. An iterative regression based method is proposed to detect and correct the failures according to the features. Experimental evaluation on an open dataset shows a global performance improvement of the tracking algorithm.