  In this paper we present a method for localisation of facial landmarks on
human and sheep. We introduce a new feature extraction scheme called
triplet-interpolated feature used at each iteration of the cascaded shape
regression framework. It is able to extract features from similar semantic
location given an estimated shape, even when head pose variations are large and
the facial landmarks are very sparsely distributed. Furthermore, we study the
impact of training data imbalance on model performance and propose a training
sample augmentation scheme that produces more initialisations for training
samples from the minority. More specifically, the augmentation number for a
training sample is made to be negatively correlated to the value of the fitted
probability density function at the sample's position. We evaluate the proposed
scheme on both human and sheep facial landmarks localisation. On the benchmark
300w human face dataset, we demonstrate the benefits of our proposed methods
and show very competitive performance when comparing to other methods. On a
newly created sheep face dataset, we get very good performance despite the fact
that we only have a limited number of training samples and a set of sparse
landmarks are annotated.
