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In this paper we present a method for localisation of facial landmarks onhuman and sheep. We introduce a new feature extraction scheme calledtriplet-interpolated feature used at each iteration of the cascaded shaperegression framework. It is able to extract features from similar semanticlocation given an estimated shape, even when head pose variations are large andthe facial landmarks are very sparsely distributed. Furthermore, we study theimpact of training data imbalance on model performance and propose a trainingsample augmentation scheme that produces more initialisations for trainingsamples from the minority. More specifically, the augmentation number for atraining sample is made to be negatively correlated to the value of the fittedprobability density function at the sample's position. We evaluate the proposedscheme on both human and sheep facial landmarks localisation. On the benchmark300w human face dataset, we demonstrate the benefits of our proposed methodsand show very competitive performance when comparing to other methods. On anewly created sheep face dataset, we get very good performance despite the factthat we only have a limited number of training samples and a set of sparselandmarks are annotated.

Human and Sheep Facial Landmarks Localisation by Triplet Interpolated Features