A ranking model for face alignment with Pseudo Census Transform

We extend the PCT (Pseudo Census Transform)-based appearance model [3] to ranking-based appearance model for face alignment. The PCT-based weak ranking function is learned using RankSVM, and the ranking appearance model (RAM) is constructed in a boosting manner. Experiments show that the PCT-based RAM is more robust and generalize better than the PCT-based boosted appearance model (BAM). The PCT-RAM achieves about 23% improvement when tested on unseen data. We also investigate different sampling strategies for the learning to rank problem and find out that random permutation achieves similar results as using adjacent ordering pairs. The alignment results do not decrease significantly when only one ordinal pair is used for each direction.